Merging Smart-seq2 and 10X Datasets - wild-type only
Gametocyte Development in Plasmodium berghei
Andrew Russell
August 09, 2021
- 1. Introduction and Aims
- 2. Read in the data
- 3. Merging the Smart-seq2 and 10X Data
- 4. Dimensionality reduction
- 5. Clustering
- 6. Define Cluster Identities
- 7. Pseudotime on all cells
- 8. Plots
- 9. Subset sexual cells
- 10. Sex assignment of mutants
- 11. Save and Export
- Appendix
- Expression plots
- Density Plots
1. Introduction and Aims
We have quality-controlled the 10X data and the SS2 data and now are left with the following objects:
10X 5K data - pb_sex_filtered
10X 30K data - pb_30k_sex_filtered
SS2 mutant data - ss2_mutants_final
2. Read in the data
Load/Install the Required Packages
[1] "patchwork is loaded correctly"
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Key
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parLapplyLB, parRapply, parSapply, parSapplyLB
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anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
Reduce, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")',
and for packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
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contents
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Assays
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screen hits
## EDIT - change this to the excel table once we have it finalized for the screen
screen_hits <- c("PBANKA-0516300",
"PBANKA-1217700",
"PBANKA-0409100",
"PBANKA-1034300",
"PBANKA-1437500",
"PBANKA-0827500",
"PBANKA-0824300",
"PBANKA-1426900",
"PBANKA-0105300",
"PBANKA-0921100",
"PBANKA-1002400",
"PBANKA-0829400",
"PBANKA-1347200",
"PBANKA-0828000",
"PBANKA-0902300",
"PBANKA-1418100",
"PBANKA-1435200",
"PBANKA-1454800",
"PBANKA-0712300",
"PBANKA-0410500",
"PBANKA-1144800",
"PBANKA-1231600",
"PBANKA-0503200",
"PBANKA-0308900",
"PBANKA-1214700",
"PBANKA-0709900",
"PBANKA-0311900",
"PBANKA-0716500",
"PBANKA-1447900",
"PBANKA-0102200",
"PBANKA-0713500",
"PBANKA-0102400",
"PBANKA-1302700",
"PBANKA-1235900",
"PBANKA-0401100",
"PBANKA-0413400",
"PBANKA-1126900",
"PBANKA-1425900",
"PBANKA-0418300",
"PBANKA-1464600",
"PBANKA-0806000")Read in gene annotations
gene_annotations <- read.table("../data/Reference/GenesByTaxon_Summary.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)
dim(gene_annotations)
## convert _ to -
gene_annotations$Gene.ID <- gsub("_", "-", gene_annotations$Gene.ID)load in datasets
paste("10x dataset")
[1] "10x dataset"pb_sex_filteredAn object of class Seurat
5098 features across 6191 samples within 1 assay
Active assay: RNA (5098 features, 2000 variable features)
2 dimensional reductions calculated: pca, umappaste("Smart-seq2 dataset")[1] "Smart-seq2 dataset"ss2_mutants_finalAn object of class Seurat
5245 features across 2717 samples within 1 assay
Active assay: RNA (5245 features, 2000 variable features)
2 dimensional reductions calculated: pca, umappaste("The composition of the Smart-seq2 dataset is:")[1] "The composition of the Smart-seq2 dataset is:"table(ss2_mutants_final@meta.data$genotype)
Mutant WT
2028 689 3. Merging the Smart-seq2 and 10X Data
Prepare data
tenx_5k_counts_to_integrate
An object of class Seurat
5098 features across 6191 samples within 1 assay
Active assay: RNA (5098 features, 0 variable features)We need to make sure the mutant data is compatible with the 10X data. the 10X data has fewer genes represented so we need to find the intersect of the two before integration.
GCSKO_mutants
An object of class Seurat
5018 features across 2717 samples within 1 assay
Active assay: RNA (5018 features, 0 variable features)## double check that this is the same number of genes
## subset counts so that only genes represented in the other two objects are there:
length(intersect(rownames(tenx_5k_counts), rownames(mutant_counts_for_integration)))[1] 5018IMPORTANT - this next step is different to GCSKO_merge as it subsets the smart-seq2 data into wild-type only.
## subset wt on ss2 data:
ss2_wt_cells <- rownames(GCSKO_mutants@meta.data[GCSKO_mutants@meta.data$genotype == "WT", ])
GCSKO_mutants_wtonly <- subset(GCSKO_mutants, cells = ss2_wt_cells)create list and normalise:
## make list
tenx.justwt.list <- list(tenx_5k_counts_to_integrate, GCSKO_mutants_wtonly)
## prepare data
for (i in 1:length(tenx.justwt.list)) {
tenx.justwt.list[[i]] <- NormalizeData(tenx.justwt.list[[i]], verbose = FALSE)
tenx.justwt.list[[i]] <- FindVariableFeatures(tenx.justwt.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
all.genes <- rownames(tenx.justwt.list[[i]])
tenx.justwt.list[[i]] <- ScaleData(tenx.justwt.list[[i]], features = all.genes)
}Integrate objects
## Find anchors
tenx.justwt.anchors <- FindIntegrationAnchors(object.list = tenx.justwt.list, dims = 1:21, verbose = FALSE)
## Integrate data
tenx.justwt.integrated <- IntegrateData(anchorset = tenx.justwt.anchors, dims = 1:21, verbose = FALSE, features.to.integrate = genes_in_tenx_dataset)4. Dimensionality reduction
PCA
Optimised UMAP
After optimisation, the following UMAP can be calculated:
## Run optimised UMAP
tenx.justwt.integrated <- RunUMAP(tenx.justwt.integrated, reduction = "pca", dims = 1:10, n.neighbors = 50, seed.use = 1234, min.dist = 0.4, repulsion.strength = 0.03, local.connectivity = 150)
Make final plots:
## split seurat object up
ob.list <- SplitObject(tenx.justwt.integrated, split.by = "experiment")
## make plots for each object
plot.list <- lapply(X = ob.list, FUN = function(x) {
DimPlot(x, dims = c(1,2), pt.size = 1) + theme(legend.position="bottom")
})
composition_umap_10x <- plot.list$`tenx_5k` +
coord_fixed() +
theme_void() +
scale_color_manual(values=c(replicate(45, "#999999"))) +
labs(title = paste("10x (wild-type)")) +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold"))
composition_umap_ss2 <- plot.list$mutants +
coord_fixed() +
theme_void() +
scale_color_manual(values=c(replicate(46, "#999999"))) +
labs(title = paste("Smart-seq2 (wild-type)")) +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold"))
composition_umap <- composition_umap_10x + composition_umap_ss2
composition_umap
save
ggsave("../images_to_export/composition_umap.png", plot = composition_umap, device = "png", path = NULL, scale = 1, width = 20, height = 20, units = "cm", dpi = 300, limitsize = TRUE)## make plots
## hoo dataset correlation
UMAP_hoo <- DimPlot(tenx.justwt.integrated, pt.size = 0.1, group.by = "Prediction.Spearman.", dims = c(1,2)) +
coord_fixed() +
theme_void() +
labs(title = paste("Hoo Predicted Timepoint")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_manual(values = inferno(12)) +
labs(colour = "hour") +
theme(legend.position = "bottom", legend.title=element_text(size=10))
## ap2g timecourse in this paper correlation
UMAP_kasia <- DimPlot(tenx.justwt.integrated, pt.size = 0.1, group.by = "Prediction.Spearman._Kasia", dims = c(1,2)) +
coord_fixed() +
theme_void() +
labs(title = paste("AP2G Timecourse Predicted Timepoint")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_manual(values = inferno(10)) +
labs(colour = "hour") +
theme(legend.position = "bottom", legend.title=element_text(size=10))
## combine
umap_bulk <- wrap_plots(UMAP_hoo, UMAP_kasia, ncol = 2)
## print
umap_bulk
MCA Mapping
Build the index
## load in mca data
counts <- read.csv("../data/Reference/scmap/allpb10x_counts.csv", row.names = 1)
pheno <- read.csv("../data/Reference/scmap/allpb10x_pheno.csv")
## change dash
rownames(counts) <- gsub("_", "-", rownames(counts))
## load MCA pal
mca_colors <- c("6"="#78C679",
"2"="#D1EC9F",
"0"="#FEB24C",
"1"="#F4CF63",
"3"="#FEEEAA",
"4"="#85B1D3",
"7"="#9ecae1",
"5"="#C9E8F1",
"M"= "#B7B7D8",
"F"="#9C96C6",
"unassigned"="darkgrey")
## plot
ggplot(pheno, aes(x=PC2_3d, y = PC3_3d, colour=absclust3)) +
geom_point() +
theme_classic() +
scale_color_manual(values = mca_colors, labels = (c("0 - early troph", "1 - mid troph","2 - late ring", "3 - late troph","4 - early schizont", "5 - late schizont", "6 - early ring","7 - mid schizont", "female", "male", "unassigned")))
###Making an ortholog reference index
## load required libraries
library(scmap)Creating a generic function for ‘toJSON’ from package ‘jsonlite’ in package ‘googleVis’library(SingleCellExperiment)Loading required package: SummarizedExperiment
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anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
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Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
see 'citation("Biobase")', and for packages 'citation("pkgname")'.
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Attaching package: ‘SummarizedExperiment’
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Assays#prep the SCE, if was originally a Suerat object need the dfs to be regular matrices
#pb_filtered_sce_orth <- pb_filtered_sce_orth[, colData(pb_filtered_sce_orth)$absclust3 != "8"]
#sce <- pb_filtered_sce_orth
#pca <- plotPCA(sce)
#pcs <- pca$data
#table(rownames(pcs)==colnames(sce))
#colData(sce) <- cbind(colData(sce), pcs)
#rowData(sce)$feature_symbol <- rowData(sce)$gene
sce <- SingleCellExperiment(list(counts=counts),
colData=DataFrame(label=pheno),
rowData=DataFrame(feature_symbol=rownames(counts)))
sceclass: SingleCellExperiment
dim: 4890 4884
metadata(0):
assays(1): counts
rownames(4890): PBANKA-0000301 PBANKA-0000600 ... PBANKA-MIT0350 PBANKA-MIT0360
rowData names(1): feature_symbol
colnames(4884): AAACCTGAGCACCGTC AAACCTGAGCGCTTAT ... AGCTTGACAATAGAGT CCGGGATCACGTTGGC
colData names(21): label.X label.sample_id ... label.PC2_3d label.PC3_3d
reducedDimNames(0):
altExpNames(0):counts_1 <- assay(sce, "counts")
libsizes <- colSums(counts_1)
size.factors <- libsizes/mean(libsizes)
logcounts(sce) <- log2(t(t(counts_1)/size.factors) + 1)
#counts(sce) <- as.matrix(counts(sce))
#logcounts(sce) <- as.matrix(logcounts(sce))
# remove features with duplicated names
sce <- sce[!duplicated(rownames(sce)), ]
#build scmap-cell reference index, save this rds
sce <- selectFeatures(sce, suppress_plot = FALSE, n_features = 500)
table(rowData(sce)$scmap_features)
FALSE TRUE
4390 500 set.seed(1)
sce <- indexCell(sce)Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)names(metadata(sce)$scmap_cell_index)[1] "subcentroids" "subclusters" length(metadata(sce)$scmap_cell_index$subcentroids)[1] 50dim(metadata(sce)$scmap_cell_index$subcentroids[[1]])[1] 10 69metadata(sce)$scmap_cell_index$subcentroids[[1]][,1:5] 1 2 3 4 5
PBANKA-0100021 0.000000000 0.002174527 0.00000000 0.000000000 0.001882238
PBANKA-0100400 0.674777171 0.000000000 0.71839411 0.000000000 0.000000000
PBANKA-0100700 0.000000000 0.000000000 0.06346152 0.000000000 0.003406199
PBANKA-0100900 0.026157737 0.000000000 0.03578261 0.000000000 0.439794449
PBANKA-0102700 0.000000000 0.000000000 0.00000000 0.007797631 0.010978344
PBANKA-0103200 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000
PBANKA-0105100 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000
PBANKA-0107300 0.007199727 0.999997636 0.65054584 0.999969598 0.898011255
PBANKA-0108800 0.734420349 0.000000000 0.23174764 0.000000000 0.004578116
PBANKA-0109900 0.067575567 0.000000000 0.04105225 0.000000000 0.000000000dim(metadata(sce)$scmap_cell_index$subclusters)[1] 50 4884#saveRDS(pb_filtered_sce_orth, file="pb_filtered_sce_orthindex_20181109.rds")Map to the index
## convert seurat object above into sce for this
pf_field_counts <- as.matrix(tenx.justwt.integrated@assays$RNA@data)
pf_field_pheno <- tenx.justwt.integrated@meta.data
#prep the SCE, if was originally a Suerat object need the dfs to be regular matrices
#pb_filtered_sce_orth <- pb_filtered_sce_orth[, colData(pb_filtered_sce_orth)$absclust3 != "8"]
#sce <- pb_filtered_sce_orth
#pca <- plotPCA(sce)
#pcs <- pca$data
#table(rownames(pcs)==colnames(sce))
#colData(sce) <- cbind(colData(sce), pcs)
#rowData(sce)$feature_symbol <- rowData(sce)$gene
pf_live_field.sce.orth <- SingleCellExperiment(list(counts=pf_field_counts),
colData=DataFrame(label=pf_field_pheno),
rowData=DataFrame(feature_symbol=rownames(pf_field_counts)))
pf_live_field.sce.orthclass: SingleCellExperiment
dim: 5098 6880
metadata(0):
assays(1): counts
rownames(5098): PBANKA-0000101 PBANKA-0000301 ... PBANKA-MIT0360 PBANKA-MIT0370
rowData names(1): feature_symbol
colnames(6880): AAACCTGGTAAGGGCT AAACCTGGTGCACTTA ... SC25027_8_95 SC25027_8_96
colData names(126): label.orig.ident label.nCount_RNA ... label.pt_id_cols
label.seurat_clusters_dot_plotting
reducedDimNames(0):
altExpNames(0):counts_1 <- assay(pf_live_field.sce.orth, "counts")
libsizes <- colSums(counts_1)
size.factors <- libsizes/mean(libsizes)
logcounts(pf_live_field.sce.orth) <- log2(t(t(counts_1)/size.factors) + 1)
#Project query data set onto cell index
scmapCell_results <- scmapCell(
pf_live_field.sce.orth,
list(mca = metadata(sce)$scmap_cell_index
)
)
##Look into the results
# For each dataset there are two matricies. cells matrix contains the top 10 (scmap default) cell IDs of the cells of the reference dataset that a given cell of the projection dataset is closest to:
#
# Give assignments in two ways:
# 1. Take the top cell assignment abs clust, if cosine similarity is less than 0.4 (or adjust if needed) mark as unassigned
# 2. For the top 3 nearest neighbors, get a mean of the PCA coordinates and snap to the nearest cell of those coordinates. If any of the top three cells are sim below 0.4 then mark as unassigned.
##Top cell assignment method
scmapCell_results$mca$cells[, 1:3] AAACCTGGTAAGGGCT AAACCTGGTGCACTTA AAACGGGTCGTCCAGG
[1,] 1591 2340 2220
[2,] 2310 2185 2122
[3,] 4699 2242 2350
[4,] 2137 1591 2242
[5,] 2121 2430 1653
[6,] 2092 2108 2457
[7,] 1091 1094 2175
[8,] 4680 2090 2170
[9,] 2226 4122 2365
[10,] 2136 2226 2226getcells <- scmapCell_results$mca$cells[1, ]
cdsce <- colData(sce)[getcells, ]
topsim <- scmapCell_results$mca$similarities[1, ]
pf_live_field.sce.orth$topcell <- cdsce$label.sample_id
pf_live_field.sce.orth$topcell_ac <- cdsce$label.absclust3
pf_live_field.sce.orth$indexPC1 <- cdsce$label.PC1
pf_live_field.sce.orth$indexPC2 <- cdsce$label.PC2
#pf_live_field.sce.orth$pbpt <- cdsce$pseudotime
#pf_live_field.sce.orth$pbbulk <- cdsce$bulk
pf_live_field.sce.orth$topcell_sp <- pf_live_field.sce.orth$topcell_ac
pf_live_field.sce.orth$topsim <- topsim
pf_live_field.sce.orth$topcell_sp[pf_live_field.sce.orth$topsim < 0.3] <- "unassigned"
table(pf_live_field.sce.orth$topcell_sp)
0 1 2 3 4 5 6 7 F M
1766 534 3478 123 73 71 103 37 353 342 add these metrics back into the object
## add the meta data from scmap to the object
# live
## extract
df_plot_live <- data.frame(topcell_sp = colData(pf_live_field.sce.orth)$topcell_sp, topsim = colData(pf_live_field.sce.orth)$topsim, row.names = rownames(colData(pf_live_field.sce.orth)))
## add to object
tenx.justwt.integrated <- AddMetaData(
object = tenx.justwt.integrated,
metadata = df_plot_live,
col.name = c('mca_topcell_sp', 'mca_topsim')
)
## need to fix the NA in topcell_sp
# Un-factorize (as.numeric can be use for numeric values)
# (as.vector can be use for objects - not tested)
tenx.justwt.integrated@meta.data$mca_topcell_sp <- as.character(tenx.justwt.integrated@meta.data$mca_topcell_sp)
## add unassigned
tenx.justwt.integrated@meta.data$mca_topcell_sp[is.na(tenx.justwt.integrated@meta.data$mca_topcell_sp)] <- "unassigned"
# Re-factorize with the as.factor function or simple factor(fixed$Type)
tenx.justwt.integrated@meta.data$mca_topcell_sp <- as.factor(tenx.justwt.integrated@meta.data$mca_topcell_sp)
## check
table(tenx.justwt.integrated@meta.data$mca_topcell_sp)
0 1 2 3 4 5 6 7 F M
1766 534 3478 123 73 71 103 37 353 342 ## reorder levels
tenx.justwt.integrated@meta.data$mca_topcell_sp <- factor(tenx.justwt.integrated@meta.data$mca_topcell_sp, levels = rev(c("M", "F", "5", "7", "4", "3", "1", "0", "2", "6")))
UMAP_mca <- DimPlot(tenx.justwt.integrated, pt.size = 0.1, group.by = "mca_topcell_sp", dims = c(1,2)) +
coord_fixed() +
theme_void() +
labs(title = paste("MCA Predicted Cell Type"), colour = "MCA Cell Type") +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_manual(values = mca_colors, labels = (c("6 - early ring", "2 - late ring", "0 - early troph", "1 - mid troph", "3 - late troph", "4 - early schizont", "7 - mid schizont", "5 - late schizont", "female", "male"))) +
theme(legend.position = "right", legend.title=element_text(size=15))
UMAP_mca
save
ggsave("../images_to_export/UMAP_mca.png", plot = UMAP_mca, device = "png", path = NULL, scale = 1, width = 15, height = 15, units = "cm", dpi = 300, limitsize = TRUE)5. Clustering
Generate clusters
## generate new clusters at low resolution
tenx.justwt.integrated <- FindNeighbors(tenx.justwt.integrated, dims = 1:19, reduction = "pca")
tenx.justwt.integrated <- FindClusters(tenx.justwt.integrated, resolution = 2, random.seed = 42, algorithm = 2)visualise clusters
DimPlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE, repel = FALSE, label = TRUE)Make individual plots highlighting where cells in each cluster fall
plot
## this function writes the next bit of code for you
## put it into the console and paste the response
#ploty <- c()
#for(i in seq_along(levels(tenx.justwt.integrated@meta.data$seurat_clusters))){
# ploty <- paste0(ploty, "list_UMAPs_by_cluster[[", i, "]]", " + ")
#}
## plot
list_UMAPs_by_cluster[[1]] + list_UMAPs_by_cluster[[2]] + list_UMAPs_by_cluster[[3]] + list_UMAPs_by_cluster[[4]] + list_UMAPs_by_cluster[[5]] + list_UMAPs_by_cluster[[6]] + list_UMAPs_by_cluster[[7]] + list_UMAPs_by_cluster[[8]] + list_UMAPs_by_cluster[[9]] + list_UMAPs_by_cluster[[10]] + list_UMAPs_by_cluster[[11]] + list_UMAPs_by_cluster[[12]] + list_UMAPs_by_cluster[[13]] + list_UMAPs_by_cluster[[14]] + list_UMAPs_by_cluster[[15]] + list_UMAPs_by_cluster[[16]] + list_UMAPs_by_cluster[[17]] + list_UMAPs_by_cluster[[18]] + list_UMAPs_by_cluster[[19]] + list_UMAPs_by_cluster[[20]] + list_UMAPs_by_cluster[[21]] + list_UMAPs_by_cluster[[22]] + list_UMAPs_by_cluster[[23]] + list_UMAPs_by_cluster[[24]]Find Markers
markers_annotated <- merge(pb.markers, gene_annotations, by.x = "gene", by.y = "Gene.ID", all = FALSE)
View(markers_annotated)markers_annotated <- merge(pb.markers, gene_annotations, by.x = "gene", by.y = "Gene.ID", all = FALSE)
View(markers_annotated)markers_subset <- pb.markers %>% group_by(cluster) %>% top_n(n = 20, wt = avg_logFC)
markers_subset_annotated <- merge(markers_subset, gene_annotations, by.x = "gene", by.y = "Gene.ID", all = FALSE)
markers_subset_annotated
View(markers_subset_annotated)View(markers_annotated_mutant <- markers_annotated[which(markers_annotated$gene %in% list_of_mutant_genes), ])visualise markers
## get the top 10 for each cluster
top10 <- pb.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
## scale data is missing - but original dfs have scale.data
# df_scaled <- as.matrix(transform(merge(as.data.frame(tenx.justwt.list[[1]]@assays$RNA@scale.data), as.data.frame(tenx.justwt.list[[2]]@assays$RNA@scale.data), by = 0, all=FALSE), row.names=Row.names, Row.names=NULL))
# tenx.justwt.integrated@assays$RNA@scale.data <- df_scaled
## scale data in RNA slot
all.genes <- rownames(tenx.justwt.integrated)
tenx.justwt.integrated <- ScaleData(tenx.justwt.integrated, vars.to.regress = 'experiment', features = all.genes)
## heatmap
DoHeatmap(tenx.justwt.integrated, features = top10$gene) + NoLegend()6. Define Cluster Identities
Downsampling
# ## check number of cells in each cluster
# df_clusters <- as.data.frame(table(tenx.justwt.integrated@meta.data$integrated_snn_res.2))
# plot(df_clusters)
#
# ## downsample using appropriate metrics
# tenx.justwt.integrated.downsampled <- subset(tenx.justwt.integrated, downsample = 100)
#
# ## inspect plot
# FeaturePlot(tenx.justwt.integrated.downsampled, features = "PBANKA-1437500", coord.fixed = TRUE, dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
# theme_void() +
# labs(title = paste("AP2G (Commitment)")) +
# theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
# scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))Marker Genes
useful tools for all plots
## define male and female symbol
female_symbol <- intToUtf8(9792)
male_symbol <- intToUtf8(9794)
## define colour pal
marker_gene_ramp <- colorRampPalette(c("#D3D3D3", "#1D1564"))(50)Expression - cutoffs - purple
marker_gene_plot_CCP2 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-1319500",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(ccp2)~(Female))) +
theme(plot.title = element_text(hjust = 0.5),
legend.text = element_text(size = 5),
text=element_text(size=6.0),
plot.margin = unit(c(0, 0, 0, 0), "cm")
) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_MG1 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-0416100",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(mg1)~(Male))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_AP2G <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-1437500",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(ap2g)~(Commitment))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_MSP1 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-0831000",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(msp1)~(Schizont))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_MSP8 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-1102200",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(msp8)~(Asexual))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_SBP1 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-1101300",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(SBP1)~(Ring))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_FAMB <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-0722600",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(fam-b2)~(Ring))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_ORC1 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-0602000",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(orc1)~(DNA~replication))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_CDPK5 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-1351500",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(cdpk5)~(Schizont))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
marker_gene_plot_MCM4 <- FeaturePlot(tenx.justwt.integrated,
features = "PBANKA-1415600",
coord.fixed = TRUE,
min.cutoff = "q05",
max.cutoff = "q95",
dims = c(1,2),
reduction = "umap",
pt.size = 0.5,
order = TRUE,
slot = 'scale.data') +
coord_fixed() +
theme_void() +
labs(title = expression(italic(mcm4)~(DNA~replication))) +
theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 5), text=element_text(size=6.0), plot.margin = unit(c(0, 0, 0, 0), "cm")) +
scale_color_continuous_sequential(palette = "Purples 2", labels = number_format(accuracy = 0.1)) +
guides(colour = guide_colourbar(barwidth = 0.3, barheight = 3.0, title = ""))
# + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
## plot
## cowplot method
marker_gene_plot_all <- plot_grid(marker_gene_plot_FAMB, marker_gene_plot_MSP8, marker_gene_plot_MSP1, marker_gene_plot_AP2G, marker_gene_plot_CCP2, marker_gene_plot_MG1, marker_gene_plot_HSP70, nrow=3)
marker_gene_plot_allMutant Genes
Expression - with cutoffs
ggsave("../images_to_export/ALL_CELLS_wt_gene_expression.png", plot = mutant_expression_composite, device = "png", path = NULL, scale = 1, width = 21, height = 29.7, units = "cm", dpi = 300, limitsize = TRUE)save
ggsave("../images_to_export/ALL_CELLS_wt_gene_expression.png", plot = mutant_expression_composite, device = "png", path = NULL, scale = 1, width = 21, height = 29.7, units = "cm", dpi = 300, limitsize = TRUE)# PBANKA-1418100 GCSKO-17 FD3
# PBANKA-0102400 GCSKO-2 MD3
# PBANKA-0716500 GCSKO-19 MD4
# PBANKA-1435200 GCSKO-20 FD4
# PBANKA-0902300 GCSKO-13 FD2
# PBANKA-0413400 GCSKO-10_820 MD5
# PBANKA-0828000 GCSKO-3 GD1
# PBANKA-1302700 GCSKO-oom MD1
# PBANKA-1447900 GCSKO-29 MD2
# PBANKA-1454800 GCSKO-21 FD1
# PBANKA-1144800 GCSKO-28 FD5
marker_gene_plot_17 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1418100", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd3")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_2 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0102400", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md3")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_19 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0716500", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md4")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_20 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1435200", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd4")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_13 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0902300", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd2")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_10 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0413400", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md5")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_3 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0828000", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("gd1")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_oom <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1302700", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md1")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_29 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1447900", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md2")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_21 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1454800", coord.fixed = TRUE, min.cutoff = "q10", max.cutoff = "q90", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd1")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
##original label:
# labs(title = paste("(CCP2; Female)","\n", "PBANKA_1319500"))
## make composite plot
mutant_expression_composite <- wrap_plots(marker_gene_plot_17 , marker_gene_plot_2 , marker_gene_plot_19 , marker_gene_plot_20 , marker_gene_plot_13 , marker_gene_plot_10 , marker_gene_plot_3 , marker_gene_plot_oom , marker_gene_plot_29 , marker_gene_plot_21 , ncol = 4)
## print
mutant_expression_composite7. Pseudotime on all cells
Pseudotime calculation
## extract data from Seurat
seurat.object.all <- tenx.justwt.integrated
# counts
data <- as(as.matrix(GetAssayData(seurat.object.all, assay = "integrated", slot = "data")), 'sparseMatrix')
# meta data
pd <- data.frame(seurat.object.all@meta.data)
## keep only the columns that are relevant
#pData <- pd %>% select(orig.ident, nCount_RNA, nFeature_RNA)
## add gene short name
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
## Construct monocle cds
monocle.object.all <- new_cell_data_set(expression_data = data, cell_metadata = pd, gene_metadata = fData)
## preprocess
monocle.object.all = preprocess_cds(monocle.object.all, num_dim = 100, norm_method = "none")
## plot variance explained plot
#plot_pc_variance_explained(monocle.object.all)
## make monocle UMAP
#monocle.object.all = reduce_dimension(monocle.object.all, reduction_method = "UMAP", preprocess_method = "PCA", umap.metric = "euclidean", umap.n_neighbors = 20, umap.min_dist = 0.5, verbose = FALSE)
#plot_cells(monocle.object.all)
## add UMAP from Seurat
monocle.object.all@int_colData@listData$reducedDims@listData[["UMAP"]] <-seurat.object.all@reductions[["umap"]]@cell.embeddings
plot_cells(monocle.object.all)
## cluster
monocle.object.all = cluster_cells(monocle.object.all)
## plot clusters
plot_cells(monocle.object.all, color_cells_by="partition", group_cells_by="partition", x = 1, y = 2)
## reduce partitions to 1
monocle.object.all@clusters$UMAP$partitions[monocle.object.all@clusters$UMAP$partitions == "2"] <- "1"
#map pseudotime
monocle.object.all = learn_graph(monocle.object.all, learn_graph_control=list(ncenter=550, minimal_branch_len = 15), use_partition = FALSE)
plot_cells(monocle.object.all, color_cells_by="partition", group_cells_by="partition", x = 1, y = 2)
## a helper function to identify the root principal points:
## make cluster 2 the root
# get_earliest_principal_node <- function(cds, time_bin="7"){
# cell_ids <- which(colData(cds)[, "seurat_clusters"] == time_bin)
# closest_vertex <-
# cds@principal_graph_aux[["UMAP"]]$pr_graph_cell_proj_closest_vertex
# closest_vertex <- as.matrix(closest_vertex[colnames(cds), ])
# root_pr_nodes <-
# igraph::V(principal_graph(cds)[["UMAP"]])$name[as.numeric(names
# (which.max(table(closest_vertex[cell_ids,]))))]
#
# root_pr_nodes
# }
## calculate pseudotime
#monocle.object.all = order_cells(monocle.object.all, root_pr_nodes=get_earliest_principal_node(monocle.object.all))
monocle.object.all = order_cells(monocle.object.all)
## used 5 points at the beginning
## plot
umap_pt <- plot_cells(monocle.object.all, color_cells_by = "pseudotime", label_cell_groups=FALSE, cell_size = 1, x = 1, y = 2, label_branch_points=FALSE, label_leaves=FALSE, label_groups_by_cluster=FALSE, label_roots = FALSE) +
coord_fixed() +
theme_void() +
labs(title = "") +
theme(plot.title = element_text(hjust = 0.5, size=20), legend.position="bottom", legend.title=element_text (size=20), legend.text=element_text(size=20)) +
guides(colour = guide_colourbar(barwidth = 10, barheight = 2, title = "Pseudotime"))
## view plot
umap_pt
## help was obtained from here
## https://github.com/satijalab/seurat/issues/1658save
ggsave("../images_to_export/UMAP_pt_wt.png", plot = umap_pt, device = "png", path = NULL, scale = 1, width = 20, height = 20, units = "cm", dpi = 300, limitsize = TRUE)gganimnate GIF of pseuodtime
Select appropriate cells
plot <- DimPlot(tenx.justwt.integrated, label = FALSE, repel = TRUE, pt.size = 0.1, dims = c(1,2), reduction = "umap")
HoverLocator(plot, information = FetchData(object = tenx.justwt.integrated, vars = 'monocle_sex'))the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used`error_y.color` does not currently support multiple values.`error_x.color` does not currently support multiple values.`line.color` does not currently support multiple values.The titlefont attribute is deprecated. Use title = list(font = ...) instead.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used`error_y.color` does not currently support multiple values.`error_x.color` does not currently support multiple values.`line.color` does not currently support multiple values.The titlefont attribute is deprecated. Use title = list(font = ...) instead.cells_to_remove <- c("SC26779_8_280", "CTGGTCTAGCAGATCG")#install.packages("gganimate")
library(gganimate)
#install.packages("gifski")
#install.packages("av")
#library(gifski)
#library(av)
## make dataframe for plotting
## extract data for GGplot version of this
df_animation <- as.data.frame(monocle.object.all@int_colData@listData$reducedDims@listData[["UMAP"]])
## add pt to this data frame:
pt_values <- as.data.frame(pseudotime(monocle.object.all, reduction_method = "UMAP"))
df_animation <- merge(df_animation, pt_values, by="row.names")
rownames(df_animation) <- df_animation$Row.names
colnames(df_animation)[4] <- "pt"
## make the static plot
p <- ggplot(df_animation, aes(x = UMAP_1, y = UMAP_2, colour = pt)) +
geom_point() +
scale_colour_viridis_c(option = "plasma") +
coord_fixed() +
theme_void() +
theme(legend.position = "none")
## view plot
plot(p)
## make animated plot
## make a category for animation
#df_animation$group <- cut(df_animation$pt, 15)
anim <- p +
transition_time(pt) +
shadow_mark()
animate(anim, height = 3, width = 3, units = "in", res = 150, bg = 'transparent')
## to change the resolution - https://stackoverflow.com/questions/49058567/define-size-for-gif-created-by-gganimate-change-dimension-resolution Save animation
anim_save("animated_UMAP_transparent_bg_wt.gif", path = "../images_to_export/")## extract pt values
pt_values <- as.data.frame(pseudotime(monocle.object.all, reduction_method = "UMAP"))
tenx.justwt.integrated <- AddMetaData(tenx.justwt.integrated, pt_values, "old_pt_values")
## make dataframe for plotting
## extract data for GGplot version of this
df_animation <- as.data.frame(tenx.justwt.integrated@reductions[["umap"]]@cell.embeddings)
## add pt to this data frame:
pt_values <- as.data.frame(tenx.justwt.integrated@meta.data[ ,c("old_pt_values", "pt_id_cols"), drop = FALSE])
df_animation <- merge(df_animation, pt_values, by="row.names")
rownames(df_animation) <- df_animation$Row.names
colnames(df_animation)[4] <- "pt"
## remove outlier cells
df_animation <- df_animation[which(df_animation$Row.names %ni% cells_to_remove), ]
#df_animation <- df_animation[order(df_animation$pt, decreasing = TRUE), ]
## make a category for animation
#df_animation$group <- as.numeric(cut(df_animation$pt, 10, include.lowest = TRUE, labels = c(1:10)))
## make the static plot
p <- ggplot(df_animation, aes(x = UMAP_1, y = UMAP_2)) +
geom_point(col = df_animation$pt_id_cols) +
#scale_colour_viridis_c(option = "plasma") +
coord_fixed() +
theme_void() +
theme(legend.position = "none") +
view_static()
## view plot
plot(p)
## make animated plot
anim <- p +
transition_time(pt) +
shadow_mark() +
#shadow_wake(wake_length = 0.99) #+
enter_fade()
animate(anim,
height = 3,
width = 3,
units = "in",
res = 300,
bg = 'white',
#duration = 6,
fps = 20,
device = "png",
#ref_frame = 1
end_pause = 10
)
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Finalizing encoding... done!
Define cell identities with branches
Define identities of cells
male
monocle.object_male <- choose_graph_segments(monocle.object.all)female
monocle.object_female <- choose_graph_segments(monocle.object.all)bipotential
monocle.object_bipot <- choose_graph_segments(monocle.object.all)asexual (pre-branch)
monocle.object_asex_pre <- choose_graph_segments(monocle.object.all)asexual fate
monocle.object_asex_fate <- choose_graph_segments(monocle.object.all)check
df_freq <- data.frame(table(c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex_pre), colnames(monocle.object_asex_fate))))
paste("number of cells in seurat object is", length(colnames(monocle.object.all)), ". The number of cells selected here with an identitity is", dim(df_freq)[1])
df_freq <- df_freq[df_freq$Freq > 1, ]
df_freqInspect where these missing cells are:
# '%ni%' <- Negate('%in%')
#
# not_assigned_cells <- colnames(monocle.object.all)[colnames(monocle.object.all) %ni% c(colnames(monocle.object_male), colnames(monocle.object_female), colnames(monocle.object_bipot), colnames(monocle.object_asex), colnames(monocle.object_asex_fate))]
#
# DimPlot(seurat.object, repel = TRUE, label.size = 5, pt.size = 0.5, cells.highlight = not_assigned_cells, dims = c(2,1), reduction = "DIM_UMAP") +
# coord_fixed() +
# scale_color_manual(values=c("#000000", "#f54e1e"))## create annotation dataframe from these results:
df_monocle_sexes <- rbind(data.frame("cell_name" = colnames(monocle.object_male), "sex" = rep("Male", length(colnames(monocle.object_male)))),
data.frame("cell_name" = colnames(monocle.object_female), "sex" = rep("Female", length(colnames(monocle.object_female)))),
data.frame("cell_name" = colnames(monocle.object_bipot), "sex" = rep("Bipotential", length(colnames(monocle.object_bipot)))),
data.frame("cell_name" = colnames(monocle.object_asex_pre), "sex" = rep("Asexual_Early", length(colnames(monocle.object_asex_pre)))),
data.frame("cell_name" = colnames(monocle.object_asex_fate), "sex" = rep("Asexual_Late", length(colnames(monocle.object_asex_fate))))
#data.frame("cell_name" = not_assigned_cells, "sex" = rep("Unassigned", length(not_assigned_cells)))
)
dim(df_monocle_sexes)
## order like the metadata
df_monocle_sexes <- df_monocle_sexes[match(rownames(monocle.object.all@colData), df_monocle_sexes$cell_name), ]
## add this back into the monocle object
monocle.object.all@colData$Sexes_monocle <- df_monocle_sexes$sex
## add this to the seurat object
rownames(df_monocle_sexes) <- df_monocle_sexes$cell_name
df_monocle_sexes_to_add_to_seurat <- df_monocle_sexes[,c("sex"), drop = FALSE]
tenx.justwt.integrated <- AddMetaData(tenx.justwt.integrated, df_monocle_sexes_to_add_to_seurat, col.name = "monocle_sex")pt vs. real time
gg <- ggplot(df_plotting, aes(as.factor(Prediction.Spearman.), fill = monocle_sex)) + geom_bar(stat = "count", position="fill")
gg
gg <- ggplot(df_plotting, aes(as.factor(Prediction.Spearman.), fill = monocle_sex)) + geom_bar(stat = "count", position="fill")
gg8. Plots
make composite pseudotime/ID figure
# 1 = blue - "#0052c5"
# 2 = red - "#a52b1e"
# 3 = green - "#016c00"
# 4 = yellow - "#ffe400"
#pal_sex <- c("#0052c5","#ffe400", "#a52b1e", "#016c00")
## extract pseudotime numbers and identity of cells to a dataframe
df_pt_id <- tenx.justwt.integrated@meta.data[,c("old_pt_values", "monocle_sex")]
## inspect possible values
list_of_sexes <- names(table(df_pt_id$monocle_sex))
## make a new column
df_pt_id$colour <- NA
## make colour ramps
asex_ramp <- colorRampPalette(c("#D5E3F5", "#0052c5"))
male_ramp <- colorRampPalette(c("white", "yellow", "#016c00"))
female_ramp <- colorRampPalette(c("yellow", "#a52b1e"))
bipot_ramp <- colorRampPalette(c("white", "#ffe400"))
## re-classify the cells that are unassigned cells removed from sexual branch above:
#df_pt_id[which(rownames(df_pt_id) %in% remove_cells), ]$monocle_sex <- "Asexual"
## assign values to each cluster
## help here: https://stackoverflow.com/questions/9946630/colour-points-in-a-plot-differently-depending-on-a-vector-of-values
df_pt_id[df_pt_id$monocle_sex == "Asexual_Early" | df_pt_id$monocle_sex == "Asexual_Late", ]$colour <- asex_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Asexual_Early" | df_pt_id$monocle_sex == "Asexual_Late", ]$old_pt_values,breaks = 100))]
df_pt_id[df_pt_id$monocle_sex == "Male", ]$colour <- male_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Male", ]$old_pt_values,breaks = 100))]
df_pt_id[df_pt_id$monocle_sex == "Female", ]$colour <- female_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Female", ]$old_pt_values,breaks = 100))]
df_pt_id[df_pt_id$monocle_sex == "Bipotential", ]$colour <- bipot_ramp(100)[as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Bipotential", ]$old_pt_values,breaks = 100))]
## check everything has a value
#table(is.na(df_pt_id$colour))
## make into a df
#df_pt_id <- df_pt_id[ ,"colour", drop = FALSE]
## add back to seurat object
df <- df_pt_id[ ,"colour", drop = FALSE]
tenx.justwt.integrated <- AddMetaData(tenx.justwt.integrated, df, "pt_id_cols")
rm(df)
## plot
## extract UMAP coords
df_umap_plot <- tenx.justwt.integrated@reductions[["umap"]]@cell.embeddings
df_umap_plot <- merge(df_umap_plot, df_pt_id, by=0, all=TRUE)
## add tree
##The tree for monocle is located here:
# monocle.object@principal_graph_aux[["UMAP"]]$dp_mst
ica_space_df <- t(monocle.object.all@principal_graph_aux[["UMAP"]]$dp_mst) %>%
as.data.frame() %>%
dplyr::select_(prin_graph_dim_1 = "UMAP_1", prin_graph_dim_2 = "UMAP_2") %>%
dplyr::mutate(sample_name = rownames(.),
sample_state = rownames(.))
dp_mst <- monocle.object.all@principal_graph[["UMAP"]]
edge_df <- dp_mst %>%
igraph::as_data_frame() %>%
dplyr::select_(source = "from", target = "to") %>%
dplyr::left_join(ica_space_df %>%
dplyr::select_(
source="sample_name",
source_prin_graph_dim_1="prin_graph_dim_1",
source_prin_graph_dim_2="prin_graph_dim_2"),
by = "source") %>%
dplyr::left_join(ica_space_df %>%
dplyr::select_(
target="sample_name",
target_prin_graph_dim_1="prin_graph_dim_1",
target_prin_graph_dim_2="prin_graph_dim_2"),
by = "target")
## make ggplot
umap_id_pt <- ggplot(df_umap_plot, aes(x = UMAP_1, y = UMAP_2)) +
geom_point(col = df_umap_plot$colour) +
theme_void() +
coord_fixed() +
geom_segment(aes_string(x="source_prin_graph_dim_1",
y="source_prin_graph_dim_2",
xend="target_prin_graph_dim_1",
yend="target_prin_graph_dim_2"),
data=edge_df)
umap_id_ptsave
#ggsave("../images_to_export/umap_id_pt.png", plot = umap_id_pt, device = "png", path = NULL, scale = 1, width = 21, height = 29.7, units = "cm", dpi = 300, limitsize = TRUE)cluster plot
### Prepare data
## extract df with cluster ID,
df_pt_id <- tenx.justwt.integrated@meta.data[ ,"seurat_clusters", drop=FALSE]
## extract UMAP coords
df_plot <- tenx.justwt.integrated@reductions[["umap"]]@cell.embeddings
df_plot <- merge(df_plot, df_pt_id, by=0, all=TRUE)
## add colour col
df_plot$colour <- df_plot$seurat_clusters
levels(df_plot$colour) <- list(print(asex_ramp(17)[1]) = "9", asex_ramp(17)[2]="4",
asex_ramp(17)[3]="15",
asex_ramp(17)[4] = "8",
asex_ramp(17)[5]= "1",
asex_ramp(17)[6]= "14",
asex_ramp(17)[7] = "2",
asex_ramp(17)[8] = "10",
asex_ramp(17)[9] = "3",
asex_ramp(17)[10] = "0",
asex_ramp(17)[11] = "6",
asex_ramp(17)[12] = "5",
asex_ramp(17)[13] = "7",
asex_ramp(17)[14] = "12",
asex_ramp(17)[15] = "18",
asex_ramp(17)[16] = "20",
asex_ramp(17)[17] = "23",
bipot_ramp(3)[2] = "11",
male_ramp(3)[2] = "16",
male_ramp(3)[3] = "13",
female_ramp(4)[2] = "21",
female_ramp(4)[3] = "22",
female_ramp(4)[4] = "19",
female_ramp(4)[4] = "17"
)
## aabbreviate clusters
df_plot$cluster_names <- df_plot$seurat_clusters
levels(df_plot$cluster_names) <- list(A_1="9",
A_2="4",
A_3="15",
A_4 = "8",
A_5 = "1",
A_6= "14",
A_7 = "2",
A_8 = "10",
A_9 = "3",
A_10 = "0",
A_11 = "6",
A_12 = "5",
A_13 = "7",
A_14 = "12",
A_15 = "18",
A_16 = "20",
A_17 = "23",
B = "11",
M_1 = "16",
M_2 = "13",
F_1 = "21",
F_2 = "22",
F_3 = "19",
F_3 = "17"
)
library(dplyr)
dat%>%
group_by(custid)%>%
summarise(Mean=mean(value), Max=max(value), Min=min(value), Median=median(value), Std=sd(value))
## plot
umap_id <- ggplot(df_plot, aes(x = UMAP_1, y = UMAP_2)) +
geom_point(col = df_plot$colour) +
theme_void() +
coord_fixed()
umap_idggsave("../images_to_export/umap_with_clusters.png", plot = umap_with_clusters, device = "png", path = NULL, scale = 1, width = 21, height = 29.5, units = "cm", dpi = 300, limitsize = TRUE)save
ggsave("../images_to_export/umap_with_clusters.png", plot = umap_with_clusters, device = "png", path = NULL, scale = 1, width = 21, height = 29.5, units = "cm", dpi = 300, limitsize = TRUE)## extract proportion of cells
library(plyr)
df_prop_comb <- as.data.frame(table(tenx.justwt.integrated@meta.data$seurat_clusters_dot_plotting, tenx.justwt.integrated@meta.data$experiment))
names(df_prop_comb) <- c("cluster", "experiment", "Freq")
## calcualte the percentage
df_prop_comb$pc <- NA
df_prop_comb[df_prop_comb$experiment == "mutants", ]$pc <- (df_prop_comb[df_prop_comb$experiment == "mutants", ]$Freq/sum(df_prop_comb[df_prop_comb$experiment == "mutants", ]$Freq))*100
df_prop_comb[df_prop_comb$experiment == "tenx_5k", ]$pc <- (df_prop_comb[df_prop_comb$experiment == "tenx_5k", ]$Freq/sum(df_prop_comb[df_prop_comb$experiment == "tenx_5k", ]$Freq))*100
## reorder levels in Var1
#df_prop_comb$cluster <- factor(df_prop_comb$cluster, levels = c("unassigned", "M", "F", "5", "7", "4", "3", "1", "0", "2", "6"))
## then reorder by this so the cumsum will work below
df_prop_comb <- df_prop_comb[rev(order(df_prop_comb$experiment, df_prop_comb$cluster)),]
## Calculate the cumulative sum of len for each dose
df_cumsum <- ddply(df_prop_comb, "experiment", transform, label_ypos=cumsum(pc) - 0.5*pc)
head(df_cumsum)
# http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization
## reverse the levels in Var1
#df_cumsum$cluster <- factor(df_cumsum$cluster, levels = rev(c(levels(df_cumsum$cluster))))
#df_cumsum <- df_cumsum[c(match(df_cumsum$cluster[1:23], c(levels(df_cumsum$cluster))), match(df_cumsum$cluster[1:23], c(levels(df_cumsum$cluster))) + 23), ]
library(ggrepel)
library(ggpubr)
## make plot
plot_prop <- ggplot(data=df_cumsum, aes(x=experiment, y=pc, fill=cluster)) +
geom_bar(stat="identity")+
geom_label_repel(aes(label = Freq, y=label_ypos, fill = factor(cluster)), color = c(rep(c(rep(c("#ffffff"), 2), "#000000", "#ffffff", rep(c("#000000"), 2), rep(c("#ffffff"), 5), rep(c("#000000"), 12)), 2)), size = 3.5, show.legend = FALSE) +
#geom_text(aes(y=label_ypos, label=Freq), vjust=0, color="black", size=3.5) +
labs(fill = "Cell cluster", y= "Cell Proportions (%)", x = "Technology") +
scale_fill_manual(labels = c("Asexual 1", "Asexual 2" , "Asexual 3", "Asexual 4", "Asexual 5", "Asexual 6", "Asexual 7", "Asexual 8", "Asexual 9", "Asexual 10", "Asexual 11", "Asexual 12", "Asexual 13", "Asexual 14", "Asexual 15", "Asexual 16", "Asexual 17", "Progenitor", "Male 1", "Male 2", "Female 1", "Female 2", "Female 3"), values = pal_plot) +
theme_pubr() +
theme(legend.text=element_text(size=10), legend.position="bottom") +
scale_x_discrete(labels= rev(c("10x", "Smart-seq2"))) +
coord_flip() +
guides(fill=guide_legend(ncol=9))
plot_propggsave("../images_to_export/WT_cell_type_proportions.png", plot = plot_prop, device = "png", path = NULL, scale = 1, width = 30, height = 9, units = "cm", dpi = 300, limitsize = TRUE)dotplot
A dotplot allows us to look at the expression of multiple genes in a clearer way than succesive UMAP plots
### Data set-up
# PBANKA-0828000 GCSKO-3 GD1
# PBANKA-1302700 GCSKO-oom MD1
# PBANKA-1447900 GCSKO-29 MD2
# PBANKA-0413400 GCSKO-10_820 MD3
# PBANKA-0716500 GCSKO-19 MD4
# PBANKA-0102400 GCSKO-2 MD5
# PBANKA-1454800 GCSKO-21 FD1
# PBANKA-0902300 GCSKO-13 FD2
# PBANKA-1418100 GCSKO-17 FD3
# PBANKA-1435200 GCSKO-20 FD4
# PBANKA-1437500 - AP2G - commitment
# PBANKA-1319500 - CCP2 - female - used in 820 line
# PBANKA-0416100 - MG1 - dynenin heavy chain - male - used in 820 line
# PBANKA-0831000 - MSP1 - late asexual
# PBANKA-1102200 - MSP8 - early asexual (from Bozdech paper)
marker_genes_list <- c("PBANKA-1437500", "PBANKA-1319500", "PBANKA-0416100", "PBANKA-0831000", "PBANKA-1102200")
mutant_genes_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0413400", "PBANKA-0716500", "PBANKA-0102400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")
## these get defined later on, but are replicated above here for plotting
asexual_early_clusters <- c(9, 4, 15, 8, 1, 14, 2, 10, 3, 0, 6, 5)
asexual_late_clusters <- c(7, 12, 18, 20, 23)
bipotentional_early_clusters <- # 0?
bipotential_clusters <- c(11)
male_clusters <- c(16, 13)
female_clusters <- c(21, 22, 17, 19)
## copy the clusters so you don't permanently edit the master
tenx.justwt.integrated@meta.data$seurat_clusters_dot_plotting <- tenx.justwt.integrated@meta.data$seurat_clusters
## reorder the levels so you can plot the cluters as you wish
my_levels <- c(asexual_early_clusters,asexual_late_clusters, bipotential_clusters, male_clusters, female_clusters)
## reorder the levels
tenx.justwt.integrated@meta.data$seurat_clusters_dot_plotting <- factor(x = tenx.justwt.integrated@meta.data$seurat_clusters_dot_plotting, levels = my_levels)
## rename clusters so that they are intuitive
# this trick is from here: http://www.cookbook-r.com/Manipulating_data/Renaming_levels_of_a_factor/
levels(tenx.justwt.integrated@meta.data$seurat_clusters_dot_plotting) <- list(Asexual_1="9",
Asexual_2="4",
Asexual_3="15",
Asexual_4 = "8",
Asexual_5 = "1",
Asexual_6= "14",
Asexual_7 = "2",
Asexual_8 = "10",
Asexual_9 = "3",
Asexual_10 = "0",
Asexual_11 = "6",
Asexual_12 = "5",
Asexual_13 = "7",
Asexual_14 = "12",
Asexual_15 = "18",
Asexual_16 = "20",
Asexual_17 = "23",
Progenitor = "11",
Male_1 = "16",
Male_2 = "13",
Female_1 = "21",
Female_2 = "22",
Female_3 = "19",
Female_3 = "17"
)
### Annotation set-up
## extract pseudotime numbers and identity of cells to a dataframe
df_pt_id <- tenx.justwt.integrated@meta.data[,c("old_pt_values", "monocle_sex", "seurat_clusters_dot_plotting", "Prediction.Spearman.", "Prediction.Spearman._Kasia")]
## make a new column
df_pt_id$colour <- NA
## assign bins to each of the values
## help here: https://stackoverflow.com/questions/9946630/colour-points-in-a-plot-differently-depending-on-a-vector-of-values
df_pt_id[df_pt_id$monocle_sex == "Asexual_Early" | df_pt_id$monocle_sex == "Asexual_Late", ]$colour <- as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Asexual_Early" | df_pt_id$monocle_sex == "Asexual_Late", ]$old_pt_values,breaks = 100))
df_pt_id[df_pt_id$monocle_sex == "Male", ]$colour <- as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Male", ]$old_pt_values,breaks = 100))
df_pt_id[df_pt_id$monocle_sex == "Female", ]$colour <- as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Female", ]$old_pt_values,breaks = 100))
df_pt_id[df_pt_id$monocle_sex == "Bipotential", ]$colour <- as.numeric(cut(df_pt_id[df_pt_id$monocle_sex == "Bipotential", ]$old_pt_values,breaks = 100))
## make colour ramps
asex_ramp <- colorRampPalette(c("#D5E3F5", "#0052c5"))
male_ramp <- colorRampPalette(c("white", "yellow", "#016c00"))
female_ramp <- colorRampPalette(c("yellow", "#a52b1e"))
bipot_ramp <- colorRampPalette(c("white", "#ffe400"))
## assign values to each cluster
## take the mean of the bin
df_annotation <- aggregate(df_pt_id[, "colour"], list(df_pt_id$seurat_clusters_dot_plotting), mean)
## BECAUSE we have ordered the clusters already, we can simply take the row index for this bit
df_annotation$colour <- NA
df_annotation[1:17, ]$colour <- asex_ramp(100)[df_annotation[1:17, ]$x]
df_annotation[18, ]$colour <- bipot_ramp(100)[df_annotation[18, ]$x]
df_annotation[19:20, ]$colour <- male_ramp(100)[df_annotation[19:20, ]$x]
df_annotation[21:23, ]$colour <- female_ramp(100)[df_annotation[21:23, ]$x]
## plot annotation
## this really helped: https://www.biostars.org/p/396810/
h2 <- ggplot(df_annotation)+
geom_bar(mapping = aes(x = Group.1, y = 1),
stat = "identity",
fill = df_annotation$colour,
col = "#FFFFFF",
width = 1)+
theme_void()+
theme(panel.spacing.x = unit(1, "mm"))#+
#facet_grid(.~colour, scales = "free_x")
#legend <- plot_grid(get_legend(h2), get_legend(h1), ncol = 1)
h2 <- h2 + theme(legend.position = "none")
# geom_point(col = df_umap_plot$colour)
h1 <- ggplot(df_annotation)+
geom_point(mapping = aes(x = Group.1, y = 1),
col = df_annotation$colour,
size = 5)+
theme_void()+
theme(panel.spacing.x = unit(1, "mm"))#+
#facet_grid(.~colour, scales = "free_x")
#legend <- plot_grid(get_legend(h2), get_legend(h1), ncol = 1)
h1 <- h1 + theme(legend.position = "none")
# geom_point(col = df_umap_plot$colour)
## add predicted time point
## take the mean of the bin
df_annotation <- aggregate(df_pt_id[, "Prediction.Spearman."], list(df_pt_id$seurat_clusters_dot_plotting), mean)
df_annotation$colour <- NA
df_annotation$colour <- viridis(300)[(df_annotation$x)*10]
h3 <- ggplot(df_annotation)+
geom_bar(mapping = aes(x = Group.1, y = 1),
stat = "identity",
fill = df_annotation$colour,
col = "#FFFFFF",
width = 1)+
theme_void()+
theme(panel.spacing.x = unit(1, "mm"))#+
#facet_grid(.~colour, scales = "free_x")
legend_h3 <- get_legend(h3)
h3 <- h3 + theme(legend.position = "none")
h3 <- ggplot(data = df_annotation,
mapping = aes(x = Group.1, fill=x)
) +
geom_bar(col = "#FFFFFF", width = 1)+
theme_void() +
theme(panel.spacing.x = unit(1, "mm")) +
scale_fill_viridis(option = 'cividis')
legend_h3 <- get_legend(h3)
h3 <- h3 + theme(legend.position = "none")
## add kasia data
## take the mean of the bin
df_annotation <- aggregate(df_pt_id[, "Prediction.Spearman._Kasia"], list(df_pt_id$seurat_clusters_dot_plotting), mean)
df_annotation$colour <- NA
df_annotation$colour <- viridis(300)[(df_annotation$x)*10]
h4 <- ggplot(df_annotation)+
geom_bar(mapping = aes(x = Group.1, y = 1),
stat = "identity",
fill = df_annotation$colour,
col = "#FFFFFF",
width = 1)+
theme_void()+
theme(panel.spacing.x = unit(1, "mm"))+
facet_grid(.~colour, scales = "free_x")
legend <- plot_grid(get_legend(h4), ncol = 1)
legend_h4 <- get_legend(h4)
h4 <- h4 + theme(legend.position = "none")
h4 <- ggplot(data = df_annotation,
mapping = aes(x = Group.1, fill=x)
) +
geom_bar(col = "#FFFFFF", width = 1)+
theme_void() +
theme(panel.spacing.x = unit(1, "mm")) +
scale_fill_viridis()
legend_h4 <- get_legend(h4)
h4 <- h4 + theme(legend.position = "none")
### Plot
dot_plot_markers <- DotPlot(tenx.justwt.integrated,
features = c(marker_genes_list, rev(mutant_genes_list)),
group.by = "seurat_clusters_dot_plotting",
dot.min = 0.00001,
assay = 'RNA') +
theme_classic() +
coord_fixed() +
coord_flip() +
# change appearance and remove axis elements, and make room for arrows
theme(axis.text.x = element_text(size=16, angle = 45, hjust=1,vjust=1, family = "Arial"),
axis.text.y = element_text(size=16, face="italic"),
text=element_text(size=16, family="Arial", colour="black"),
legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
plot.title = element_blank(),
plot.margin = unit(c(1,3,1,3), "lines"),
axis.line = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=0.5)) +
#change the colours
#scale_colour_viridis(option = "inferno", guide = "colourbar", na.value="white", begin = 0, end = 1, direction = 1) +
scale_color_continuous_sequential(palette = "Purples 2") +
## change x axis label
# labs(x = "Marker Genes", y = "Cluster", title = "Expression of Marker Genes by Cluster")
labs(x = "", y = "", title = "") +
## add arrows
#annotate("segment", x = 5.5, xend = 5.5, y = 21.5, yend = 25, colour = "green", size=1, alpha=1, arrow=arrow(length=unit(0.30,"cm"), type = "closed")) +
#annotate("segment", x = 5.5, xend = 5.5, y = 16.5, yend = 21.5, colour = "red", size=1, alpha=1, arrow=arrow(length=unit(0.30,"cm"), type = "closed")) +
#annotate("segment", x = 5.5, xend = 5.5, y = 0, yend = 15.5, colour = "grey", size=1, alpha=1, arrow=arrow(length=unit(0.30,"cm"), type = "closed")) +
## annotate asex
geom_hline(aes(yintercept = (length(c(asexual_early_clusters, asexual_late_clusters))+0.5)), size = 0.5, linetype= 'dashed') +
## annotate bipotential
geom_hline(aes(yintercept = (length(c(asexual_early_clusters, asexual_late_clusters, bipotential_clusters))+0.5)), size = 0.5, linetype= 'dashed') +
## annotate sexes
geom_hline(aes(yintercept = (length(c(asexual_early_clusters, asexual_late_clusters, bipotential_clusters, male_clusters))+0.5)), size = 0.5, linetype= 'dashed') +
## change label on bottom of plot so we can indicate markers
scale_x_discrete(labels = rev(c("gd1", "md1", "md2", "md3", "md4", "md5", "fd1", "fd2", "fd3", "fd4", "msp8", "msp1", "mg1", "ccp2", "ap2g"))) +
## change label on bottom of plot so we can indicate markers
scale_y_discrete(labels = c("Asexual 1", "Asexual 2" , "Asexual 3", "Asexual 4", "Asexual 5", "Asexual 6", "Asexual 7", "Asexual 8", "Asexual 9", "Asexual 10", "Asexual 11", "Asexual 12", "Asexual 13", "Asexual 14", "Asexual 15", "Asexual 16", "Asexual 17", "Progenitor", "Male 1", "Male 2", "Female 1", "Female 2", "Female 3")) +
## change name of legends
guides(col=guide_colorbar(title = 'Scaled Average Expression'),
size=guide_legend("% of cells expressing"))
## view
#print(dot_plot_markers)
plot <- plot_grid(h4, h3, h1, dot_plot_markers, align = "v", ncol = 1, axis = "tb", rel_heights = c(0.5, 0.5, 0.5, 18))
dot_plot <- plot_grid(plot, legend_h3, legend_h4, nrow = 1, rel_widths = c(10, 1.5, 1.5))
dot_plotsave
ggsave("../images_to_export/dot_plot_all.png", plot = dot_plot, device = "png", path = NULL, scale = 1, width = 29.7, height = 21, units = "cm", dpi = 300, limitsize = TRUE)Make individual plots highlighting where cells in each cluster fall
plot
## this function writes the next bit of code for you
## put it into the console and paste the response
#ploty <- c()
#for(i in seq_along(levels(tenx.justwt.integrated@meta.data$seurat_clusters))){
# ploty <- paste0(ploty, "list_UMAPs_by_cluster[[", i, "]]", " + ")
#}
## plot
composite_plot <- plot_grid(list_UMAPs_by_cluster[[1]], list_UMAPs_by_cluster[[2]], list_UMAPs_by_cluster[[3]], list_UMAPs_by_cluster[[4]], list_UMAPs_by_cluster[[5]], list_UMAPs_by_cluster[[6]], list_UMAPs_by_cluster[[7]], list_UMAPs_by_cluster[[8]], list_UMAPs_by_cluster[[9]], list_UMAPs_by_cluster[[10]], list_UMAPs_by_cluster[[11]], list_UMAPs_by_cluster[[12]], list_UMAPs_by_cluster[[13]], list_UMAPs_by_cluster[[14]], list_UMAPs_by_cluster[[15]], list_UMAPs_by_cluster[[16]], list_UMAPs_by_cluster[[17]], list_UMAPs_by_cluster[[18]], list_UMAPs_by_cluster[[19]], list_UMAPs_by_cluster[[20]], list_UMAPs_by_cluster[[21]], list_UMAPs_by_cluster[[22]], list_UMAPs_by_cluster[[23]], ncol = 8)
composite_plotsave
ggsave("../images_to_export/clusters_composite_plot.png", plot = composite_plot, device = "png", path = NULL, scale = 1, width = 21, height = 10, units = "cm", dpi = 300, limitsize = TRUE)9. Subset sexual cells
Make a subsetted Seurat object of sexual cells.
Include the pre-branch too as well as any weird clusters that may have clustered out.
it’s been a while since we looked at the clusters so let’s check them out again:
## Plot
DimPlot(tenx.justwt.integrated, label = TRUE, repel = FALSE, pt.size = 0.05, group.by = "seurat_clusters", dims = c(1,2), reduction = "umap") + coord_fixed()
## plot
list_UMAPs_by_cluster[[1]] + list_UMAPs_by_cluster[[2]] + list_UMAPs_by_cluster[[3]] + list_UMAPs_by_cluster[[4]] + list_UMAPs_by_cluster[[5]] + list_UMAPs_by_cluster[[6]] + list_UMAPs_by_cluster[[7]] + list_UMAPs_by_cluster[[8]] + list_UMAPs_by_cluster[[9]] + list_UMAPs_by_cluster[[10]] + list_UMAPs_by_cluster[[11]] + list_UMAPs_by_cluster[[12]] + list_UMAPs_by_cluster[[13]] + list_UMAPs_by_cluster[[14]] + list_UMAPs_by_cluster[[15]] + list_UMAPs_by_cluster[[16]] + list_UMAPs_by_cluster[[17]] + list_UMAPs_by_cluster[[18]] + list_UMAPs_by_cluster[[19]] + list_UMAPs_by_cluster[[20]] + list_UMAPs_by_cluster[[21]] + list_UMAPs_by_cluster[[22]] + list_UMAPs_by_cluster[[23]] + list_UMAPs_by_cluster[[24]]Define cells and subset
asexual_early_clusters <- c(9, 4, 15, 8, 1, 14, 2, 10, 3, 0, 6, 5)
asexual_late_clusters <- c(7, 12, 18, 20, 23)
bipotentional_early_clusters <- # 0?
bipotential_clusters <- c(11)
male_clusters <- c(16, 13)
female_clusters <- c(21, 22, 17, 19)
## define cells
cell_names_subset_monocle_ids <- rownames(tenx.justwt.integrated@meta.data[tenx.justwt.integrated@meta.data$monocle_sex %in% c("Asexual_Early", "Bipotential", "Male", "Female"), ])
## 3, 0, 6, 5 are early clusters of asexuals before the branch
cell_names_subset_cluster_ids <- rownames(tenx.justwt.integrated@meta.data[tenx.justwt.integrated@meta.data$seurat_clusters %in% c(male_clusters, female_clusters, bipotential_clusters, 3, 0, 6, 5), ])
cell_names_subset_intersect <- intersect(cell_names_subset_monocle_ids, cell_names_subset_cluster_ids)
## subset cells into new object
tenx.justwt.integrated.sex <- subset(tenx.justwt.integrated, cells = cell_names_subset_intersect)inspect/check
## inspect object
tenx.justwt.integrated.sex
## look at original UMAP
DimPlot(tenx.justwt.integrated.sex, label = TRUE, repel = TRUE, pt.size = 0.1, split.by = "experiment", dims = c(1,2), reduction = "umap") + coord_fixed()Remove contaminant asexual cells
we want to remove:
## look at original UMAP
plot_sexual_subsetting <- DimPlot(tenx.justwt.integrated.sex, label = TRUE, repel = TRUE, pt.size = 0.1, dims = c(1,2), reduction = "umap") +
coord_fixed() +
geom_vline(aes(xintercept = -1, alpha = 5))
plot_sexual_subsetting## extract cell embeddings
df_sex_cell_embeddings <- as.data.frame(tenx.justwt.integrated.sex@reductions[["umap"]]@cell.embeddings)
## subset anything lower than -0.8 in UMAP 2 and -0.1 in UMAP 1
remove_cells <- row.names(df_sex_cell_embeddings[which(df_sex_cell_embeddings$UMAP_1 < -1), ])
## plot these cells
DimPlot(tenx.justwt.integrated.sex, label = FALSE, repel = TRUE, pt.size = 0.1, cells.highlight = remove_cells, dims = c(1,2), reduction = "umap") +
coord_fixed() +
scale_color_manual(values=c("#000000", "#f54e1e")) +
theme_void() +
labs(title = paste("cells highlighted will be removed")) +
theme(plot.title = element_text(hjust = 0.5), legend.position = "none")Final Subset
## make keep cells from the remove_cells
## make the not in function
'%ni%' <- Negate('%in%')
keep_cells <- colnames(tenx.justwt.integrated.sex)[which(colnames(tenx.justwt.integrated.sex) %ni% remove_cells)]
## subset
tenx.justwt.integrated.sex <- subset(tenx.justwt.integrated.sex, cells = keep_cells)
## inspect
tenx.justwt.integrated.sexcopy old clusters over
## copy old clusters
tenx.justwt.integrated.sex <- AddMetaData(tenx.justwt.integrated.sex, tenx.justwt.integrated.sex@meta.data$seurat_clusters, col.name = "post_integration_clusters")10. Sex assignment of mutants
A. Seurat Method
## see how this overlaps with fluorescence sorted on for 820 to confirm accuracy
df_820 <- df[df$genetic_background =="PBANKA_820", ]
table(df_820$predicted.id, df_820$fluoresence_sorted_on)
GFP Hoechst mCherry
Asexual_Early 0 69 1
Asexual_Late 33 459 16
Female 2 342 240
Male 182 49 0## see how this overlaps with fluorescence sorted on for 820 to confirm accuracy
df_820 <- df[df$genetic_background =="PBANKA_820", ]
table(df_820$predicted.id, df_820$fluoresence_sorted_on)inspect gd1
df[which(df$predicted.id == "Female_1" & df$identity_name_updated == "gd1"), ][, 118:146]Calculate sex ratios
## use designations above for sexes
male_cells <- rownames(mutant_seurat@meta.data[mutant_seurat@meta.data$predicted.id == "Male", ])
female_cells <- rownames(mutant_seurat@meta.data[mutant_seurat@meta.data$predicted.id == "Female", ])
ss2_mutants_final_male <- subset(mutant_seurat, cells = male_cells)
ss2_mutants_final_female <- subset(mutant_seurat, cells = female_cells)
## inspect
ss2_mutants_final_male
ss2_mutants_final_female## calculate sex ratios
##subset out H, sorted cells:
df_male <- ss2_mutants_final_male@meta.data[ss2_mutants_final_male@meta.data$exclude_for_sex_ratio == FALSE,]
dim(df_male)
df_female <- ss2_mutants_final_female@meta.data[ss2_mutants_final_female@meta.data$exclude_for_sex_ratio == FALSE,]
dim(df_female)
## make dataframe
df_sex_ratio <- merge(
as.data.frame(table(df_male$sub_name_updated)),
as.data.frame(table(df_female$sub_name_updated)),
by = "Var1", all=TRUE)
# or use identity_updated
## add names
names(df_sex_ratio) <- c("genotype", "male", "female")
## change the NAs to 0
df_sex_ratio[is.na(df_sex_ratio)] <- 0
## collapse 820 wild-types together
combined_m <- df_sex_ratio[df_sex_ratio$genotype == "WT-820_3_5", ]$male + df_sex_ratio[df_sex_ratio$genotype == "WT-820", ]$male
combined_f <- df_sex_ratio[df_sex_ratio$genotype == "WT-820_3_5", ]$female + df_sex_ratio[df_sex_ratio$genotype == "WT-820", ]$female
df_sex_ratio <- rbind(df_sex_ratio, c("WT-820-combined", combined_m, combined_f))
# remove old rows
df_sex_ratio <- df_sex_ratio[-which(df_sex_ratio$genotype == "WT-820_3_5" | df_sex_ratio$genotype == "WT-820"), ]
# need to make numeric again
df_sex_ratio$male <- as.numeric(df_sex_ratio$male)
df_sex_ratio$female <- as.numeric(df_sex_ratio$female)
df_sex_ratio$genotype <- as.character(df_sex_ratio$genotype)
# add name for WT combined
df_sex_ratio$genotype[17] <- "WT-820-combined"
## calculate sex ratio
df_sex_ratio$sex_ratio <- (df_sex_ratio$male + 0.1)/(df_sex_ratio$female + 0.1)
## log sex ratio
df_sex_ratio$sex_ratio_log <- log10(df_sex_ratio$sex_ratio)
##view
df_sex_ratio## remove WT-2 because it is really inappropriate to have a sex ratio for this:
df_sex_ratio <- df_sex_ratio[-which(df_sex_ratio$genotype == "WT-md5"),]plot
library(latex2exp) # so you can plot the fraction, it's on CRAN `install.packages("latex2exp")`
## make extra column for plotting aesthetics:
df_sex_ratio$above <- df_sex_ratio$sex_ratio_log > 0
## make extra column with ratio in it:
df_sex_ratio$genotype_with_n <- paste0(df_sex_ratio$genotype, " (", df_sex_ratio$male, "/", df_sex_ratio$female, ")")
## reorder genotype so it is in the correct order for plotting
df_sex_ratio$genotype_with_n <- factor(df_sex_ratio$genotype_with_n, levels = df_sex_ratio$genotype_with_n[order(df_sex_ratio$sex_ratio_log)])
## plot
sex_ratio_plot <- ggplot(df_sex_ratio, aes(sex_ratio_log, genotype_with_n, color = above)) +
## add the lines for the lollipop plot
geom_segment(aes(x = 0, y = genotype_with_n, xend = sex_ratio_log, yend = genotype_with_n), color = "grey50") +
## add the points for the lollipop plot
geom_point(aes(size = 4)) +
## add the wild-type rectangle
annotate("rect", xmin= -0.23182478, xmax = 1.00105797, ymin=-Inf , ymax=Inf, alpha=0.4, color=NA,linetype = 2, fill="#999999") +
## make prettier
theme_classic() +
theme(legend.position = "none", text=element_text(size=16, family="Arial")) +
## change axis labels
labs(y = TeX("$Genotype\ \\left(\\frac{n_{male}}{n_{female}}\\right)$")) +
#labs(y = expression(paste("Genotype", group("(", frac(paste("n male"), "n female"), ")")))) +
xlab(expression(paste("Sex Ratio (", log[10],
group("(",
frac(paste("n male + 0.1"),
paste("n female + 0.1")),
")"), ")" ))) +
## change colours of lollipops
scale_colour_manual(values = c("#a52b1e", "#016c00")) +
## annotate phenotypes
geom_hline(aes(yintercept = 5.5)) #+
#geom_hline(aes(yintercept = 14.5))
print(sex_ratio_plot)
### CHANGE TO BIG BRACKET HERE: https://www.overleaf.com/learn/latex/Brackets_and_Parentheses
#paste("Sex Ratio", "\n", "log10((n male + 0.1)/(n female + 0.1))")
#theme(legend.position = "none", text=element_text(size=16, family="Arial"))
## fraction titles: https://groups.google.com/forum/#!topic/ggplot2/bgNRnZ82hJY
## https://uc-r.github.io/lollipop
#ggsave(filename = "../images_to_export/sex_ratio_plot.png", device = "png", width = 7, height = 7, units = "in")B. SCMAP Method
Build the index
### Making an ortholog reference index
## load in mca data
#counts <- read.csv("../scmap/allpb10x_counts.csv", row.names = 1)
#pheno <- read.csv("../scmap/allpb10x_pheno.csv")
#ggplot(pheno, aes(x=PC2_3d, y = PC3_3d,colour=absclust3)) + geom_point()
## load required libraries
library(scmap) #https://bioconductor.org/packages/release/bioc/html/scmap.html
library(SingleCellExperiment) #
#prep the SCE, if was originally a Suerat object need the dfs to be regular matrices
#pb_filtered_sce_orth <- pb_filtered_sce_orth[, colData(pb_filtered_sce_orth)$absclust3 != "8"]
#sce <- pb_filtered_sce_orth
#pca <- plotPCA(sce)
#pcs <- pca$data
#table(rownames(pcs)==colnames(sce))
#colData(sce) <- cbind(colData(sce), pcs)
#rowData(sce)$feature_symbol <- rowData(sce)$gene
## extract data from Seurat
#cells_tenx <- rownames(tenx.justwt.integrated@meta.data[which(tenx.justwt.integrated@meta.data$experiment == "tenx_5k"), ])
#tenx.justwt.integrated.10k <- subset(tenx.justwt.integrated, cells = cells_tenx)
counts = as.matrix(GetAssayData(tenx.justwt.integrated, slot = "counts", assay = "RNA"))
pheno = as.data.frame(tenx.justwt.integrated@meta.data)
## add UMAP coordinates
df_sex_cell_embeddings <- as.data.frame(tenx.justwt.integrated@reductions[["umap"]]@cell.embeddings)
pheno <- cbind(pheno, df_sex_cell_embeddings)
## Set up object
sce <- SingleCellExperiment(list(counts=counts),
colData=DataFrame(label=pheno),
rowData=DataFrame(feature_symbol=rownames(counts)))
sce
## manual logging
#counts_1 <- assay(sce, "counts")
#libsizes <- colSums(counts_1)
#size.factors <- libsizes/mean(libsizes)
#logcounts(sce) <- log2(t(t(counts_1)/size.factors) + 1)
#counts(sce) <- as.matrix(counts(sce))
#logcounts(sce) <- as.matrix(logcounts(sce))
logcounts(sce) <- as.matrix(GetAssayData(tenx.justwt.integrated, slot = "data", assay = "RNA"))
## remove features with duplicated names
sce <- sce[!duplicated(rownames(sce)), ]
## build scmap-cell reference index, save this rds
sce <- selectFeatures(sce, suppress_plot = FALSE, n_features = 2000)
table(rowData(sce)$scmap_features)
set.seed(1)
sce <- indexCell(sce, M = 50, k = 80)
names(metadata(sce)$scmap_cell_index)
#length(metadata(sce)$scmap_cell_index$subcentroids)
#dim(metadata(sce)$scmap_cell_index$subcentroids[[1]])
#metadata(sce)$scmap_cell_index$subcentroids[[1]][,1:5]
#dim(metadata(sce)$scmap_cell_index$subclusters)
#saveRDS(pb_filtered_sce_orth, file="pb_filtered_sce_orthindex_20181109.rds")Map to the index
## see how this overlaps with fluorescence sorted on for 820 to confirm accuracy
df_820 <- colData(mutants.sce)[colData(mutants.sce)$label.genetic_background =="PBANKA_820", ]
table(df_820$topcell_sp, df_820$label.fluoresence_sorted_on)
GFP Hoechst mCherry
Asexual_Early 0 89 2
Asexual_Late 5 150 3
Female 2 84 54
Male 158 47 0
unassigned 52 549 198## see how this overlaps with fluorescence sorted on for 820 to confirm accuracy
df_820 <- colData(mutants.sce)[colData(mutants.sce)$label.genetic_background =="PBANKA_820", ]
table(df_820$topcell_sp, df_820$label.fluoresence_sorted_on)table(colData(mutants.sce)$topcell_sp, colData(mutants.sce)$label.identity_name_updated)## see how this overlaps with fluorescence sorted on for 820 to confirm accuracy
df_820 <- colData(mutants.sce)[colData(mutants.sce)$label.genetic_background =="PBANKA_820", ]
table(df_820$stage_pred, df_820$label.fluoresence_sorted_on)
GFP Hoechst mCherry
Asexual_Early 1 81 2
Asexual_Late 3 153 3
Bipotential 0 1 0
Female 2 83 52
Male 156 47 0
unassigned 55 554 200## see how this overlaps with fluorescence sorted on for 820 to confirm accuracy
df_820 <- colData(mutants.sce)[colData(mutants.sce)$label.genetic_background =="PBANKA_820", ]
table(df_820$stage_pred, df_820$label.fluoresence_sorted_on)table(colData(mutants.sce)$stage_pred, colData(mutants.sce)$label.identity_name_updated)make final plot with MCA data below
## Read in MCA data
pheno <- read.csv("../scmap/allpb10x_pheno.csv")
## extract rows needed for plotting
df_plot <- pheno[,c("PC2_3d", "PC3_3d", "absclust3")]
df_plot$experiment <- df_plot$absclust3
## extract rows needed for plotting from mapped object
df_plot_pm <- as.data.frame(colData(pm_ss2_field.sce.orth))
df_plot_pm <- df_plot_pm[ ,c("X_snap", "Y_snap", "label.absclust3")]
df_plot_pm$experiment <- "pm"
colnames(df_plot_pm) <- c("PC2_3d", "PC3_3d", "absclust3", "experiment")
## extract rows needed for plotting from mapped object
df_plot_pf <- as.data.frame(colData(pf_ss2_field.sce.orth))
df_plot_pf <- df_plot_pf[ ,c("X_snap", "Y_snap", "label.absclust3")]
df_plot_pf$experiment <- "pf"
colnames(df_plot_pf) <- c("PC2_3d", "PC3_3d", "absclust3", "experiment")
## bind together
df_plot <- rbind(df_plot, df_plot_pf, df_plot_pm)
colors <- c("6"="#78C679",
"2"="#D1EC9F",
"0"="#FEB24C",
"1"="#F4CF63",
"3"="#FEEEAA",
"4"="#85B1D3",
"7"="#9ecae1",
"5"="#C9E8F1",
"M"= "#B7B7D8",
"F"="#9C96C6",
"unassigned"="black",
"pm" = "#dc65a4",
"pf" = "#24245f")
## add alpha for plotting
df_plot$alpha[!df_plot$experiment == "pm"] <- 0.5
df_plot$alpha[df_plot$experiment == "pm"] <- 1
df_plot$alpha[df_plot$experiment == "pf"] <- 1
##plot
scmap_pca <- ggplot(df_plot, aes(x=PC2_3d, y = PC3_3d,colour=experiment, alpha = alpha)) +
geom_point() +
theme_void() +
scale_color_manual(values = colors) +
## remove alpha scale
scale_alpha_continuous(guide=FALSE)
## draw ring around field samples
#geom_point(data=df_plot[df_plot$experiment == "pm", ], pch=21, fill=NA, size=2, colour="black", stroke=1) +
## make non-field samples more opaque
#geom_point(data=df_plot[-df_plot$experiment == "pm", ], alpha = 0.2)
## view
scmap_pca_2 <- scmap_pca +
guides(colour=guide_legend(override.aes = list(size=4)))
scmap_pca_2save
#ggsave("../images_to_export/field_samples_scmap.png", plot = scmap_pca_2, device = "png", path = NULL, scale = 1, width = 15, height = 10, units = "cm", dpi = 300, limitsize = TRUE)Overlap and validation
11. Save and Export
save
Save environment
## This saves everything in the global environment for easy recall later
#save.image(file = "GCSKO_merge.RData")
#load(file = "GCSKO_merge.RData")Save object(s)
## Save an object to a file
saveRDS(tenx.justwt.integrated.sex, file = "../data_to_export/tenx.justwt.integrated.sex.RDS")
## Restore the object
#readRDS(file = "../data_to_export/tenx.mutant.integrated.sex.RDS")
## save integrated object to file
saveRDS(tenx.justwt.integrated, file = "../data_to_export/tenx.justwt.integrated.RDS")
## restore the object
#tenx.justwt.integrated <- readRDS("../data_to_export/tenx.justwt.integrated.RDS")
## save monocle object
saveRDS(monocle.object.all, file = "../data_to_export/monocle.object.all.RDS")
## restore the object
#monocle.object.all <- readRDS("../data_to_export/monocle.object.all.RDS")
## save integrated object to file
#saveRDS(GCSKO_mutants, file = "../data_to_export/GCSKO_mutants.RDS")
## restore the object
#tenx.justwt.integrated <- readRDS("../data_to_export/tenx.justwt.integrated.RDS")
## extract UMAP coordinates and metadata
## extract metadata
mca_website_df <- tenx.justwt.integrated@meta.data
## add umap - merge on row names and then make sure row names are not dropped or added
mca_website_df <- transform(merge(mca_website_df, tenx.justwt.integrated@reductions[["umap"]]@cell.embeddings, by=0, all=TRUE), row.names=Row.names, Row.names=NULL)
## send this to sunil:
# ggplot(mca_website_df, aes(x = UMAP_1, y = UMAP_2)) +
# geom_point(col = mca_website_df$pt_id_cols) +
# theme_void() +
# coord_fixed()
## export
write.csv(mca_website_df, file = "../data_to_export/mca_website_df.csv")Clean up
#rm(ss2_wt_cells)
#rm(tenx.justwt.integrated)
#rm(tenx.justwt.list)final figure construction
Cowplot(plot_grid), patchwork(wrap_plots), and ggpubr can all allow multiple plots to be plotted together.
## A
# umap_id_pt
## B
# marker gene expression
## C
# Mutant gene expression
## D
# Modules
#Figure_A <- grid.arrange(arrangeGrob(QC_composite_plot, QC_mito_violin, QC_mito_graph, QC_by_genotype, mapping_rate_plot), nrow=3), nrow=2, heights=c(10,2))
## cowplot method
## can use this for labels: toupper(letters)[1:10]
## C. Mutant genes
mutant_genes_figure <- plot_grid(
## marker genes starts
marker_gene_plot_FAMB,
marker_gene_plot_MSP8,
marker_gene_plot_MSP1,
marker_gene_plot_AP2G,
marker_gene_plot_CCP2,
marker_gene_plot_MG1,
## mutant genes starts
marker_gene_plot_gd1,
marker_gene_plot_md1,
marker_gene_plot_md2,
marker_gene_plot_md3,
marker_gene_plot_md4,
marker_gene_plot_md5,
marker_gene_plot_fd1,
marker_gene_plot_fd2,
marker_gene_plot_fd3,
marker_gene_plot_fd4,
label_size = 1,
nrow = 4)
## labels as uppercase letters:
#c(toupper(letters)[2:17])
# labels as roman numerals:
# c(tolower(as.roman(c(2:17))
Figure_publication <- plot_grid(umap_id_pt + theme(plot.margin = unit(c(0, 0, 0, 0), "cm")),
mutant_genes_figure,
## add empty plot to give spacing
ggplot() + theme_void(),
labels = c('A', 'B'),
label_size = 12,
ncol = 2,
nrow=2,
rel_heights = c(1, 1, 4),
rel_widths = c(1, 2, 3))
Figure_publicationsave
ggsave("../images_to_export/Figure_C.png", plot = Figure_publication, device = "png", path = NULL, scale = 1, width = 21, height = 29.7, units = "cm", dpi = 300, limitsize = TRUE)layout <- "
AAABBB
CCDDEE
FFGGHH
IIJJKK
"
Figure_publication_A <- (composition_umap_10x + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 10))) +
(composition_umap_ss2 + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 10))) +
plot_layout(ncol = 2)
Figure_publication <- Figure_publication_A +
#(UMAP_hoo + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 10)) + labs(title="Predicted Asexual Cycle Real Timepoint")) +
#(UMAP_kasia + theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 10)) + labs(title="Predicted Gametocytogenesis Real Timepoint")) +
## marker genes
marker_gene_plot_FAMB +
marker_gene_plot_MSP8 +
marker_gene_plot_MSP1 +
marker_gene_plot_CDPK5 +
marker_gene_plot_AP2G +
marker_gene_plot_CCP2 +
marker_gene_plot_MG1 +
marker_gene_plot_ORC1 +
marker_gene_plot_MCM4 +
## mutant genes starts
#marker_gene_plot_gd1 +
#marker_gene_plot_md1 +
#marker_gene_plot_md2 +
#marker_gene_plot_md3 +
#marker_gene_plot_md4 +
#marker_gene_plot_md5 +
#marker_gene_plot_fd1 +
#marker_gene_plot_fd2 +
#marker_gene_plot_fd3 +
#marker_gene_plot_fd4 +
plot_layout(ncol = 6,
widths = c(1, 1, 1, 1, 1 ,1),
heights = c(2, 1, 1, 1),
design = layout
)
Figure_publicationsave
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats4 parallel grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] colourvalues_0.3.7 readxl_1.3.1 destiny_3.2.0 circlize_0.4.12
[5] gganimate_1.0.7 shiny_1.5.0 monocle3_0.2.3.0 SingleCellExperiment_1.10.1
[9] SummarizedExperiment_1.18.2 DelayedArray_0.14.1 matrixStats_0.57.0 GenomicRanges_1.40.0
[13] GenomeInfoDb_1.24.2 IRanges_2.22.2 S4Vectors_0.26.1 Biobase_2.48.0
[17] BiocGenerics_0.34.0 Nebulosa_1.2.0 reshape2_1.4.4 Hmisc_4.4-1
[21] Formula_1.2-4 survival_3.2-7 lattice_0.20-41 gridExtra_2.3
[25] dplyr_1.0.2 patchwork_1.0.1 ggplot2bdc_0.3.2 cowplot_1.1.0
[29] ggpubr_0.4.0 ggplot2_3.3.2 viridis_0.5.1 viridisLite_0.3.0
[33] Seurat_3.2.2 colorspace_1.4-1
loaded via a namespace (and not attached):
[1] ggthemes_4.2.4 coda_0.19-4 tidyr_1.1.2 knitr_1.30
[5] irlba_2.3.3 data.table_1.13.2 rpart_4.1-15 RCurl_1.98-1.2
[9] generics_0.0.2 callr_3.5.1 leidenbase_0.1.2 usethis_1.6.3
[13] RANN_2.6.1 proxy_0.4-24 future_1.19.1 spatstat.data_1.4-3
[17] httpuv_1.5.4 assertthat_0.2.1 gifski_0.8.6 xfun_0.18
[21] hms_0.5.3 evaluate_0.14 promises_1.1.1 DEoptimR_1.0-8
[25] fansi_0.4.1 progress_1.2.2 DBI_1.1.0 igraph_1.2.6
[29] htmlwidgets_1.5.2 spdep_1.1-5 purrr_0.3.4 ellipsis_0.3.1
[33] RSpectra_0.16-0 crosstalk_1.1.0.1 ks_1.12.0 backports_1.1.10
[37] deldir_0.1-29 vctrs_0.3.4 TTR_0.24.2 remotes_2.2.0
[41] ROCR_1.0-11 abind_1.4-5 RcppEigen_0.3.3.7.0 withr_2.3.0
[45] grr_0.9.5 robustbase_0.93-7 checkmate_2.0.0 vcd_1.4-8
[49] sctransform_0.3.1 xts_0.12.1 prettyunits_1.1.1 mclust_5.4.7
[53] goftest_1.2-2 cluster_2.1.0 lazyeval_0.2.2 laeken_0.5.1
[57] crayon_1.3.4 units_0.6-7 slam_0.1-47 pkgconfig_2.0.3
[61] labeling_0.4.2 tweenr_1.0.1 nlme_3.1-149 pkgload_1.1.0
[65] nnet_7.3-14 devtools_2.3.2 rlang_0.4.8 globals_0.13.1
[69] lifecycle_0.2.0 miniUI_0.1.1.1 rsvd_1.0.3 cellranger_1.1.0
[73] rprojroot_1.3-2 polyclip_1.10-0 RcppHNSW_0.3.0 lmtest_0.9-38
[77] Matrix_1.2-18 raster_3.3-13 carData_3.0-4 Matrix.utils_0.9.8
[81] boot_1.3-25 zoo_1.8-8 base64enc_0.1-3 ggridges_0.5.2
[85] GlobalOptions_0.1.2 processx_3.4.4 pheatmap_1.0.12 png_0.1-7
[89] bitops_1.0-6 KernSmooth_2.23-17 DelayedMatrixStats_1.10.1 classInt_0.4-3
[93] shape_1.4.5 stringr_1.4.0 jpeg_0.1-8.1 rstatix_0.6.0
[97] ggsignif_0.6.0 scales_1.1.1 memoise_1.1.0 magrittr_2.0.1
[101] plyr_1.8.6 hexbin_1.28.1 ica_1.0-2 gdata_2.18.0
[105] zlibbioc_1.34.0 compiler_4.0.3 RColorBrewer_1.1-2 pcaMethods_1.80.0
[109] fitdistrplus_1.1-1 cli_2.1.0 LearnBayes_2.15.1 XVector_0.28.0
[113] listenv_0.8.0 pbapply_1.4-3 ps_1.4.0 htmlTable_2.1.0
[117] ggplot.multistats_1.0.0 MASS_7.3-53 mgcv_1.8-33 tidyselect_1.1.0
[121] stringi_1.5.3 forcats_0.5.0 yaml_2.2.1 latticeExtra_0.6-29
[125] ggrepel_0.8.2 pbmcapply_1.5.0 tools_4.0.3 future.apply_1.6.0
[129] rio_0.5.16 rstudioapi_0.11 foreign_0.8-80 smoother_1.1
[133] scatterplot3d_0.3-41 farver_2.0.3 Rtsne_0.15 digest_0.6.27
[137] BiocManager_1.30.10 Rcpp_1.0.6 car_3.0-10 broom_0.7.2
[141] later_1.1.0.1 RcppAnnoy_0.0.16 httr_1.4.2 sf_0.9-6
[145] fs_1.5.0 tensor_1.5 ranger_0.12.1 reticulate_1.18
[149] splines_4.0.3 uwot_0.1.8 expm_0.999-5 spatstat.utils_1.17-0
[153] sp_1.4-4 spData_0.3.8 plotly_4.9.2.1 sessioninfo_1.1.1
[157] xtable_1.8-4 jsonlite_1.7.1 spatstat_1.64-1 testthat_2.3.2
[161] R6_2.5.0 gmodels_2.18.1 pillar_1.4.6 htmltools_0.5.1.1
[165] mime_0.9 glue_1.4.2 fastmap_1.0.1 VIM_6.1.0
[169] class_7.3-17 codetools_0.2-16 utf8_1.1.4 pkgbuild_1.1.0
[173] mvtnorm_1.1-1 tibble_3.0.4 curl_4.3 leiden_0.3.3
[177] gtools_3.8.2 zip_2.1.1 openxlsx_4.2.2 limma_3.44.3
[181] rmarkdown_2.5 desc_1.2.0 munsell_0.5.0 e1071_1.7-4
[185] GenomeInfoDbData_1.2.3 haven_2.3.1 gtable_0.3.0 Appendix
Session Info
Expression plots
Expression - raw - Viridis
## find a good ring marker, to see if there is a better one than the ones reported
#markers_ring <- FindMarkers(tenx.justwt.integrated, ident.1 = c("4", "5", "16", "11", "7", "3", "9", "0", "22"))
#head(markers_ring)
# PBANKA-1319500 - CCP2 - female - used in 820 line
# PBANKA-0416100 - MG1 - dynenin heavy chain - male - used in 820 line
# PBANKA-1437500 - AP2G - commitment
# PBANKA-0831000 - MSP1 - late asexual
# PBANKA-1102200 - MSP8 - early asexual (from Bozdech paper)
# PBANKA-0711900 - HSP70 - promoter used for GFP and RFP expression in the mutants
# PBANKA-1400400 - FAMB - ring marker - discovered by looking for marker genes in data
# PBANKA-0722600 - Fam-b2 - ring marker - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113031/
marker_gene_plot_CCP2 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1319500", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(ccp2), "(Female)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_MG1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0416100", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(mg1), "(Male)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_AP2G <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1437500", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(ap2g), "(Commitment)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_MSP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0831000", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(msp1), "(Schizont)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_MSP8 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1102200", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(msp8), "(Asexual)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_SBP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1101300", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(sbp1), "(Ring)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_FAMB <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0722600", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(fam-b2), "(Ring)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
marker_gene_plot_HSP70 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0711900", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste(italic(HSP70), "(Reporter)","\n", "PBANKA_0711900")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)))
##original label:
# labs(title = paste("(CCP2; Female)","\n", "PBANKA_1319500"))
## plot
## cowplot method
marker_gene_plot_all <- plot_grid(marker_gene_plot_FAMB, marker_gene_plot_MSP8, marker_gene_plot_MSP1, marker_gene_plot_AP2G, marker_gene_plot_CCP2, marker_gene_plot_MG1, marker_gene_plot_HSP70, nrow=3)
marker_gene_plot_all
## patchwork method
#marker_gene_plot_FAMB + marker_gene_plot_MSP8 + marker_gene_plot_MSP1 + marker_gene_plot_AP2G + marker_gene_plot_CCP2 + marker_gene_plot_MG1 + marker_gene_plot_HSP70Expression - raw - purple
marker_gene_ramp <- colorRampPalette(c("#D3D3D3", "#1D1564"))(50)
marker_gene_plot_CCP2 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1319500", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("CCP2 (Female)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_MG1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0416100", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("MG1 (Male)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_AP2G <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1437500", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("AP2G (Commitment)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_MSP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0831000", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("MSP1 (Schizont)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_MSP8 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1102200", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("MSP8 (Asexual)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_SBP1 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-1101300", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("SBP1 (Ring)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_FAMB <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0722600", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("Fam-b2 (Ring)")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
marker_gene_plot_HSP70 <- FeaturePlot(tenx.justwt.integrated, features = "PBANKA-0711900", coord.fixed = TRUE,
#min.cutoff = "q1",
dims = c(1,2), reduction = "umap", pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("(HSP70; Reporter)","\n", "PBANKA_0711900")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_colour_gradientn(colours=marker_gene_ramp)
## plot
## cowplot method
marker_gene_plot_all <- plot_grid(marker_gene_plot_FAMB, marker_gene_plot_MSP8, marker_gene_plot_MSP1, marker_gene_plot_AP2G, marker_gene_plot_CCP2, marker_gene_plot_MG1, marker_gene_plot_HSP70, nrow=3)
marker_gene_plot_allExpression - data - purple
# PBANKA-1418100 GCSKO-17 FD3
# PBANKA-0102400 GCSKO-2 MD3
# PBANKA-0716500 GCSKO-19 MD4
# PBANKA-1435200 GCSKO-20 FD4
# PBANKA-0902300 GCSKO-13 FD2
# PBANKA-0413400 GCSKO-10_820 MD5
# PBANKA-0828000 GCSKO-3 GD1
# PBANKA-1302700 GCSKO-oom MD1
# PBANKA-1447900 GCSKO-29 MD2
# PBANKA-1454800 GCSKO-21 FD1
# PBANKA-1144800 GCSKO-28 FD5
marker_gene_plot_17 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1418100", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd3")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_2 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0102400", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md3")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_19 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0716500", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md4")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_20 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1435200", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd4")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_13 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0902300", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd2")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_10 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0413400", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md5")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_3 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-0828000", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("gd1")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_oom <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1302700", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md1")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_29 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1447900", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("md2")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
marker_gene_plot_21 <- FeaturePlot(tenx.justwt.integrated, dims = c(1,2), reduction = "umap", features = "PBANKA-1454800", coord.fixed = TRUE, pt.size = 1, order = TRUE) +
theme_void() +
labs(title = paste("fd1")) +
theme(plot.title = element_text(hjust = 0.5, family="Arial", size = 20, face = "bold")) +
scale_color_continuous_sequential(palette = "Purples 2") +
## add sex symbols
annotate("text", x = 3.8, y = 1.5, label = male_symbol, size=7, color="gray") +
annotate("text", x = 2, y = 2.8, label = female_symbol, size=7, color="gray")
##original label:
# labs(title = paste("(CCP2; Female)","\n", "PBANKA_1319500"))
## make composite plot
mutant_expression_composite <- wrap_plots(marker_gene_plot_17 , marker_gene_plot_2 , marker_gene_plot_19 , marker_gene_plot_20 , marker_gene_plot_13 , marker_gene_plot_10 , marker_gene_plot_3 , marker_gene_plot_oom , marker_gene_plot_29 , marker_gene_plot_21 , ncol = 4)
## print
mutant_expression_compositeDensity Plots
Density - purple
# PBANKA-1319500 - CCP2 - female - used in 820 line
# PBANKA-0416100 - MG1 - dynenin heavy chain - male - used in 820 line
# PBANKA-1437500 - AP2G - commitment
# PBANKA-0831000 - MSP1 - late asexual
# PBANKA-1102200 - MSP8 - early asexual (from Bozdech paper)
# PBANKA-0711900 - HSP70 - promoter used for GFP and RFP expression in the mutants
# PBANKA-1400400 - FAMB - ring marker - discovered by looking for marker genes in data
# PBANKA-0722600 - Fam-b2 - ring marker - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113031/
markers_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-0413400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")
list_of_density_plots_mutant_genes <- plot_density(tenx.justwt.integrated, markers_list, joint = FALSE, combine = FALSE, dims = c(1,2), pal = "plasma", method = "ks")
## make composite plot
UMAP_composite_mutant_genes <- wrap_plots(list_of_density_plots_mutant_genes[[1]] +
coord_fixed() +
theme_void() +
labs(title = paste("gd1")) +
scale_colour_gradientn(colours=marker_gene_ramp) +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[2]] +
coord_fixed() + theme_void() +
labs(title = paste("md1")) +
scale_colour_gradientn(colours=marker_gene_ramp) +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[3]] + coord_fixed() + theme_void() + labs(title = paste("md2")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[4]] + coord_fixed() + theme_void() + labs(title = paste("md3")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[5]] + coord_fixed() + theme_void() +
labs(title = paste("md4")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[6]] + coord_fixed() + theme_void() + labs(title = paste("md5")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[7]] + coord_fixed() + theme_void() + labs(title = paste("fd1")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[8]] + coord_fixed() + theme_void() + labs(title = paste("fd2")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[9]] + coord_fixed() + theme_void() + labs(title = paste("fd3")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[10]] + coord_fixed() + theme_void() + labs(title = paste("fd4")) + scale_colour_gradientn(colours=marker_gene_ramp) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
ncol = 4)
UMAP_composite_mutant_genesDensity -viridis
# PBANKA-1319500 - CCP2 - female - used in 820 line
# PBANKA-0416100 - MG1 - dynenin heavy chain - male - used in 820 line
# PBANKA-1437500 - AP2G - commitment
# PBANKA-0831000 - MSP1 - late asexual
# PBANKA-1102200 - MSP8 - early asexual (from Bozdech paper)
# PBANKA-0711900 - HSP70 - promoter used for GFP and RFP expression in the mutants
# PBANKA-1400400 - FAMB - ring marker - discovered by looking for marker genes in data
# PBANKA-0722600 - Fam-b2 - ring marker - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113031/
markers_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-0413400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")
list_of_density_plots_mutant_genes <- plot_density(tenx.justwt.integrated, markers_list, joint = FALSE, combine = FALSE, dims = c(1,2), pal = "plasma", method = "ks")
## make composite plot
UMAP_composite_mutant_genes <- wrap_plots(list_of_density_plots_mutant_genes[[1]] +
coord_fixed() +
theme_void() +
labs(title = paste("gd1")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[2]] +
coord_fixed() + theme_void() +
labs(title = paste("md1")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[3]] + coord_fixed() + theme_void() + labs(title = paste("md2")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[4]] + coord_fixed() + theme_void() + labs(title = paste("md3")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)) )+ guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[5]] + coord_fixed() + theme_void() +
labs(title = paste("md4")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[6]] + coord_fixed() + theme_void() + labs(title = paste("md5")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[7]] + coord_fixed() + theme_void() + labs(title = paste("fd1")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[8]] + coord_fixed() + theme_void() + labs(title = paste("fd2")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[9]] + coord_fixed() + theme_void() + labs(title = paste("fd3")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[10]] + coord_fixed() + theme_void() + labs(title = paste("fd4")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
ncol = 4)
UMAP_composite_mutant_genesDensity - viridis
# PBANKA-0828000 GCSKO-3 GD1
# PBANKA-1302700 GCSKO-oom MD1
# PBANKA-1447900 GCSKO-29 MD2
# PBANKA-0102400 GCSKO-2 MD3
# PBANKA-0716500 GCSKO-19 MD4
# PBANKA-0413400 GCSKO-10_820 MD5
# PBANKA-1454800 GCSKO-21 FD1
# PBANKA-0902300 GCSKO-13 FD2
# PBANKA-1418100 GCSKO-17 FD3
# PBANKA-1435200 GCSKO-20 FD4
markers_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-0413400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")
list_of_density_plots_mutant_genes <- plot_density(tenx.justwt.integrated, markers_list, joint = FALSE, combine = FALSE, dims = c(1,2), pal = "plasma", method = "ks")
## make composite plot
UMAP_composite_mutant_genes <- wrap_plots(list_of_density_plots_mutant_genes[[1]] +
coord_fixed() +
theme_void() +
labs(title = paste("gd1")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[2]] +
coord_fixed() + theme_void() +
labs(title = paste("md1")) +
scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[3]] + coord_fixed() + theme_void() + labs(title = paste("md2")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[4]] + coord_fixed() + theme_void() + labs(title = paste("md3")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30)) )+ guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[5]] + coord_fixed() + theme_void() +
labs(title = paste("md4")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[6]] + coord_fixed() + theme_void() + labs(title = paste("md5")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[7]] + coord_fixed() + theme_void() + labs(title = paste("fd1")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[8]] + coord_fixed() + theme_void() + labs(title = paste("fd2")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[9]] + coord_fixed() + theme_void() + labs(title = paste("fd3")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[10]] + coord_fixed() + theme_void() + labs(title = paste("fd4")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
ncol = 4)
UMAP_composite_mutant_genes
## IF putting in MAIN - list_of_density_plots_mutant_genes[[2]] + coord_fixed() + theme_void() + theme(plot.title = element_text(hjust = 0.5), legend.text = element_text(size = 8), text=element_text(size=9)) + labs(title = paste("md1")) + scale_colour_gradientn(colours=c("#DCDCDC", plasma(30))) + guides(colour = guide_colourbar(barwidth = 0.3, barheight = 4.0, title = ""))# PBANKA-0828000 GCSKO-3 GD1
# PBANKA-1302700 GCSKO-oom MD1
# PBANKA-1447900 GCSKO-29 MD2
# PBANKA-0102400 GCSKO-2 MD3
# PBANKA-0716500 GCSKO-19 MD4
# PBANKA-0413400 GCSKO-10_820 MD5
# PBANKA-1454800 GCSKO-21 FD1
# PBANKA-0902300 GCSKO-13 FD2
# PBANKA-1418100 GCSKO-17 FD3
# PBANKA-1435200 GCSKO-20 FD4
markers_list <- c("PBANKA-0828000", "PBANKA-1302700", "PBANKA-1447900", "PBANKA-0102400", "PBANKA-0716500", "PBANKA-0413400", "PBANKA-1454800", "PBANKA-0902300", "PBANKA-1418100", "PBANKA-1435200")
list_of_density_plots_mutant_genes <- plot_density(tenx.justwt.integrated, markers_list, joint = FALSE, combine = FALSE, dims = c(1,2), pal = "plasma", method = "wkde", slot = 'data')
## make composite plot
UMAP_composite_mutant_genes <- wrap_plots(list_of_density_plots_mutant_genes[[1]] +
coord_fixed() +
theme_void() +
labs(title = paste("gd1")) +
scale_color_continuous_sequential(palette = "Purples 2") +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[2]] +
coord_fixed() + theme_void() +
labs(title = paste("md1")) +
scale_color_continuous_sequential(palette = "Purples 2") +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[3]] +
coord_fixed() +
theme_void() +
labs(title = paste("md2")) + scale_color_continuous_sequential(palette = "Purples 2") +
guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[4]] + coord_fixed() + theme_void() + labs(title = paste("md3")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[5]] + coord_fixed() + theme_void() +
labs(title = paste("md4")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[6]] + coord_fixed() + theme_void() + labs(title = paste("md5")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[7]] + coord_fixed() + theme_void() + labs(title = paste("fd1")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[8]] + coord_fixed() + theme_void() + labs(title = paste("fd2")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[9]] + coord_fixed() + theme_void() + labs(title = paste("fd3")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
list_of_density_plots_mutant_genes[[10]] + coord_fixed() + theme_void() + labs(title = paste("fd4")) + scale_color_continuous_sequential(palette = "Purples 2") + guides(colour = guide_colourbar(barwidth = 0.5, title = "")),
ncol = 4)
## old colour scale: scale_colour_gradientn(colours=marker_gene_ramp )
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2luZy54ID0gdW5pdCgxLCAibW0iKSkgKwogICAgICAgc2NhbGVfZmlsbF92aXJpZGlzKCkKIGxlZ2VuZF9oNCA8LSBnZXRfbGVnZW5kKGg0KQogaDQgPC0gaDQgKyB0aGVtZShsZWdlbmQucG9zaXRpb24gPSAibm9uZSIpCgojIyMgUGxvdApkb3RfcGxvdF9tYXJrZXJzIDwtIERvdFBsb3QodGVueC5qdXN0d3QuaW50ZWdyYXRlZCwgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICBmZWF0dXJlcyA9IGMobWFya2VyX2dlbmVzX2xpc3QsIHJldihtdXRhbnRfZ2VuZXNfbGlzdCkpLCAKICAgICAgICAgICAgICAgICAgICAgICAgICAgIGdyb3VwLmJ5ID0gInNldXJhdF9jbHVzdGVyc19kb3RfcGxvdHRpbmciLCAKICAgICAgICAgICAgICAgICAgICAgICAgICAgIGRvdC5taW4gPSAwLjAwMDAxLAogICAgICAgICAgICAgICAgICAgICAgICAgICAgYXNzYXkgPSAnUk5BJykgKwogIHRoZW1lX2NsYXNzaWMoKSArCiAgY29vcmRfZml4ZWQoKSArCiAgY29vcmRfZmxpcCgpICsKICAjIGNoYW5nZSBhcHBlYXJhbmNlIGFuZCByZW1vdmUgYXhpcyBlbGVtZW50cywgYW5kIG1ha2Ugcm9vbSBmb3IgYXJyb3dzCiAgdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoc2l6ZT0xNiwgYW5nbGUgPSA0NSwgaGp1c3Q9MSx2anVzdD0xLCBmYW1pbHkgPSAiQXJpYWwiKSwgCiAgICAgICAgYXhpcy50ZXh0LnkgPSBlbGVtZW50X3RleHQoc2l6ZT0xNiwgZmFjZT0iaXRhbGljIiksIAogICAgICAgIHRleHQ9ZWxlbWVudF90ZXh0KHNpemU9MTYsIGZhbWlseT0iQXJpYWwiLCBjb2xvdXI9ImJsYWNrIiksIAogICAgICAgIGxlZ2VuZC5wb3NpdGlvbiA9ICJib3R0b20iLCAKICAgICAgICBsZWdlbmQuZGlyZWN0aW9uID0gImhvcml6b250YWwiLCAKICAgICAgICBsZWdlbmQuYm94ID0gInZlcnRpY2FsIiwgCiAgICAgICAgcGxvdC50aXRsZSA9IGVsZW1lbnRfYmxhbmsoKSwgCiAgICAgICAgcGxvdC5tYXJnaW4gPSB1bml0KGMoMSwzLDEsMyksICJsaW5lcyIpLCAKICAgICAgICBheGlzLmxpbmUgPSBlbGVtZW50X2JsYW5rKCksCiAgICAgICAgcGFuZWwuYm9yZGVyID0gZWxlbWVudF9yZWN0KGNvbG91ciA9ICJibGFjayIsIGZpbGw9TkEsIHNpemU9MC41KSkgKwogICNjaGFuZ2UgdGhlIGNvbG91cnMKICAjc2NhbGVfY29sb3VyX3ZpcmlkaXMob3B0aW9uID0gImluZmVybm8iLCBndWlkZSA9ICJjb2xvdXJiYXIiLCBuYS52YWx1ZT0id2hpdGUiLCBiZWdpbiA9IDAsIGVuZCA9IDEsIGRpcmVjdGlvbiA9IDEpICsKICBzY2FsZV9jb2xvcl9jb250aW51b3VzX3NlcXVlbnRpYWwocGFsZXR0ZSA9ICJQdXJwbGVzIDIiKSArCiAgIyMgY2hhbmdlIHggYXhpcyBsYWJlbAogICMgbGFicyh4ID0gIk1hcmtlciBHZW5lcyIsIHkgPSAiQ2x1c3RlciIsIHRpdGxlID0gIkV4cHJlc3Npb24gb2YgTWFya2VyIEdlbmVzIGJ5IENsdXN0ZXIiKSAKICBsYWJzKHggPSAiIiwgeSA9ICIiLCB0aXRsZSA9ICIiKSArCiAgIyMgYWRkIGFycm93cwogICNhbm5vdGF0ZSgic2VnbWVudCIsIHggPSA1LjUsIHhlbmQgPSA1LjUsIHkgPSAyMS41LCB5ZW5kID0gMjUsIGNvbG91ciA9ICJncmVlbiIsIHNpemU9MSwgYWxwaGE9MSwgYXJyb3c9YXJyb3cobGVuZ3RoPXVuaXQoMC4zMCwiY20iKSwgdHlwZSA9ICJjbG9zZWQiKSkgKwogICNhbm5vdGF0ZSgic2VnbWVudCIsIHggPSA1LjUsIHhlbmQgPSA1LjUsIHkgPSAxNi41LCB5ZW5kID0gMjEuNSwgY29sb3VyID0gInJlZCIsIHNpemU9MSwgYWxwaGE9MSwgYXJyb3c9YXJyb3cobGVuZ3RoPXVuaXQoMC4zMCwiY20iKSwgdHlwZSA9ICJjbG9zZWQiKSkgKwogICNhbm5vdGF0ZSgic2VnbWVudCIsIHggPSA1LjUsIHhlbmQgPSA1LjUsIHkgPSAwLCB5ZW5kID0gMTUuNSwgY29sb3VyID0gImdyZXkiLCBzaXplPTEsIGFscGhhPTEsIGFycm93PWFycm93KGxlbmd0aD11bml0KDAuMzAsImNtIiksIHR5cGUgPSAiY2xvc2VkIikpICsKICAjIyBhbm5vdGF0ZSBhc2V4CiAgZ2VvbV9obGluZShhZXMoeWludGVyY2VwdCA9IChsZW5ndGgoYyhhc2V4dWFsX2Vhcmx5X2NsdXN0ZXJzLCBhc2V4dWFsX2xhdGVfY2x1c3RlcnMpKSswLjUpKSwgc2l6ZSA9IDAuNSwgbGluZXR5cGU9ICdkYXNoZWQnKSArCiAgIyMgYW5ub3RhdGUgYmlwb3RlbnRpYWwKICBnZW9tX2hsaW5lKGFlcyh5aW50ZXJjZXB0ID0gKGxlbmd0aChjKGFzZXh1YWxfZWFybHlfY2x1c3RlcnMsIGFzZXh1YWxfbGF0ZV9jbHVzdGVycywgYmlwb3RlbnRpYWxfY2x1c3RlcnMpKSswLjUpKSwgc2l6ZSA9IDAuNSwgbGluZXR5cGU9ICdkYXNoZWQnKSArCiAgIyMgYW5ub3RhdGUgc2V4ZXMKICBnZW9tX2hsaW5lKGFlcyh5aW50ZXJjZXB0ID0gKGxlbmd0aChjKGFzZXh1YWxfZWFybHlfY2x1c3RlcnMsIGFzZXh1YWxfbGF0ZV9jbHVzdGVycywgYmlwb3RlbnRpYWxfY2x1c3RlcnMsIG1hbGVfY2x1c3RlcnMpKSswLjUpKSwgc2l6ZSA9IDAuNSwgbGluZXR5cGU9ICdkYXNoZWQnKSArCiAgIyMgY2hhbmdlIGxhYmVsIG9uIGJvdHRvbSBvZiBwbG90IHNvIHdlIGNhbiBpbmRpY2F0ZSBtYXJrZXJzCiAgc2NhbGVfeF9kaXNjcmV0ZShsYWJlbHMgPSByZXYoYygiZ2QxIiwgIm1kMSIsICJtZDIiLCAibWQzIiwgIm1kNCIsICJtZDUiLCAiZmQxIiwgImZkMiIsICJmZDMiLCAiZmQ0IiwgIm1zcDgiLCAibXNwMSIsICJtZzEiLCAiY2NwMiIsICJhcDJnIikpKSArCiAgIyMgY2hhbmdlIGxhYmVsIG9uIGJvdHRvbSBvZiBwbG90IHNvIHdlIGNhbiBpbmRpY2F0ZSBtYXJrZXJzCiAgc2NhbGVfeV9kaXNjcmV0ZShsYWJlbHMgPSBjKCJBc2V4dWFsIDEiLCAiQXNleHVhbCAyIiAsICJBc2V4dWFsIDMiLCAiQXNleHVhbCA0IiwgIkFzZXh1YWwgNSIsICJBc2V4dWFsIDYiLCAiQXNleHVhbCA3IiwgIkFzZXh1YWwgOCIsICAiQXNleHVhbCA5IiwgIkFzZXh1YWwgMTAiLCAiQXNleHVhbCAxMSIsICJBc2V4dWFsIDEyIiwgIkFzZXh1YWwgMTMiLCAiQXNleHVhbCAxNCIsICJBc2V4dWFsIDE1IiwgIkFzZXh1YWwgMTYiLCAiQXNleHVhbCAxNyIsICJQcm9nZW5pdG9yIiwgIk1hbGUgMSIsICJNYWxlIDIiLCAiRmVtYWxlIDEiLCAiRmVtYWxlIDIiLCAiRmVtYWxlIDMiKSkgKwogICMjIGNoYW5nZSBuYW1lIG9mIGxlZ2VuZHMKICBndWlkZXMoY29sPWd1aWRlX2NvbG9yYmFyKHRpdGxlID0gJ1NjYWxlZCBBdmVyYWdlIEV4cHJlc3Npb24nKSwKICAgICAgICAgc2l6ZT1ndWlkZV9sZWdlbmQoIiUgb2YgY2VsbHMgZXhwcmVzc2luZyIpKQoKIyMgdmlldwojcHJpbnQoZG90X3Bsb3RfbWFya2VycykKCnBsb3QgPC0gcGxvdF9ncmlkKGg0LCBoMywgaDEsIGRvdF9wbG90X21hcmtlcnMsIGFsaWduID0gInYiLCBuY29sID0gMSwgYXhpcyA9ICJ0YiIsIHJlbF9oZWlnaHRzID0gYygwLjUsIDAuNSwgMC41LCAxOCkpIAoKZG90X3Bsb3QgPC0gcGxvdF9ncmlkKHBsb3QsIGxlZ2VuZF9oMywgbGVnZW5kX2g0LCBucm93ID0gMSwgcmVsX3dpZHRocyA9IGMoMTAsIDEuNSwgMS41KSkKCmRvdF9wbG90CmBgYAoKc2F2ZQpgYGB7cn0KZ2dzYXZlKCIuLi9pbWFnZXNfdG9fZXhwb3J0L2RvdF9wbG90X2FsbC5wbmciLCBwbG90ID0gZG90X3Bsb3QsIGRldmljZSA9ICJwbmciLCBwYXRoID0gTlVMTCwgc2NhbGUgPSAxLCB3aWR0aCA9IDI5LjcsIGhlaWdodCA9IDIxLCB1bml0cyA9ICJjbSIsIGRwaSA9IDMwMCwgbGltaXRzaXplID0gVFJVRSkKYGBgCgpNYWtlIGluZGl2aWR1YWwgcGxvdHMgaGlnaGxpZ2h0aW5nIHdoZXJlIGNlbGxzIGluIGVhY2ggY2x1c3RlciBmYWxsCmBgYHtyLCBlY2hvID0gRkFMU0UsIG1lc3NhZ2U9RkFMU0V9CiMjIGZvciBsb29wIHdoaWNoIHRha2VzIGVhY2ggY2x1c3RlciBhbmQgbWFrZXMgYSBsaXN0IG9mIGNlbGxzIGFuZCB0aGVuIHBsb3RzIGEgaGlnaGxpZ2h0ZWQgcGxvdCBhbmQgYWRkcyBpdCB0byBhIGxpc3QKCiMjIG1ha2UgYSBibGFuayBsaXN0Cmxpc3RfVU1BUHNfYnlfY2x1c3RlciA8LSB2ZWN0b3IobW9kZSA9ICJsaXN0IiwgbGVuZ3RoID0gbGVuZ3RoKGxldmVscyh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkQG1ldGEuZGF0YSRzZXVyYXRfY2x1c3RlcnNfZG90X3Bsb3R0aW5nKSkpCgojIyBmb3IgbG9vcApmb3IoaSBpbiBzZXFfYWxvbmcobGV2ZWxzKHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhJHNldXJhdF9jbHVzdGVyc19kb3RfcGxvdHRpbmcpKSl7CiAgIyMgbWFrZSBhIGxpc3Qgb2YgY2VsbHMKICBsaXN0X29mX2NlbGxzIDwtIHJvd25hbWVzKHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhW3doaWNoKHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhJHNldXJhdF9jbHVzdGVyc19kb3RfcGxvdHRpbmcgPT0gbGV2ZWxzKHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhJHNldXJhdF9jbHVzdGVyc19kb3RfcGxvdHRpbmcpW2ldKSwgXSkKICB1bWFwX3Bsb3QgPC0gRGltUGxvdCh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLCBsYWJlbCA9IEZBTFNFLCByZXBlbCA9IFRSVUUsIHB0LnNpemUgPSAwLjEsIGNlbGxzLmhpZ2hsaWdodCA9IGxpc3Rfb2ZfY2VsbHMsIGRpbXMgPSBjKDEsMiksIHJlZHVjdGlvbiA9ICJ1bWFwIikgKwogICAgIyMgZml4IGNvb3JkaW5hdGVzCiAgICBjb29yZF9maXhlZCgpICsgCiAgc2NhbGVfY29sb3JfbWFudWFsKHZhbHVlcz1jKCIjRDNEM0QzIiwgIiMxRDE1NjQiKSkgKyAKICB0aGVtZV92b2lkKCkgKyAKICBsYWJzKHRpdGxlID0gcGFzdGUobGV2ZWxzKHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhJHNldXJhdF9jbHVzdGVyc19kb3RfcGxvdHRpbmcpW2ldKSkgKyAKICB0aGVtZShwbG90LnRpdGxlID0gZWxlbWVudF90ZXh0KGhqdXN0ID0gMC41KSwgbGVnZW5kLnBvc2l0aW9uID0gIm5vbmUiKQogICMjIGFkZCB0byB0aGUgbGlzdAogIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbaV1dIDwtIHVtYXBfcGxvdAp9CgojIyBjaGVjayBudW1iZXIgb2YgY2x1c3RlcnMKbGVuZ3RoKGxpc3RfVU1BUHNfYnlfY2x1c3RlcikKYGBgCgpwbG90CmBgYHtyLCBmaWcuaGVpZ2h0ID0gMTAsIGZpZy53aWR0aCA9IDEwfQojIyB0aGlzIGZ1bmN0aW9uIHdyaXRlcyB0aGUgbmV4dCBiaXQgb2YgY29kZSBmb3IgeW91CiMjIHB1dCBpdCBpbnRvIHRoZSBjb25zb2xlIGFuZCBwYXN0ZSB0aGUgcmVzcG9uc2UKI3Bsb3R5IDwtIGMoKQojZm9yKGkgaW4gc2VxX2Fsb25nKGxldmVscyh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkQG1ldGEuZGF0YSRzZXVyYXRfY2x1c3RlcnMpKSl7CiMgIHBsb3R5IDwtIHBhc3RlMChwbG90eSwgImxpc3RfVU1BUHNfYnlfY2x1c3RlcltbIiwgaSwgIl1dIiwgIiArICIpCiN9CgojIyBwbG90CmNvbXBvc2l0ZV9wbG90IDwtIHBsb3RfZ3JpZChsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzFdXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1syXV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbM11dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzRdXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1s1XV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbNl1dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzddXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1s4XV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbOV1dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzEwXV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbMTFdXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1sxMl1dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzEzXV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbMTRdXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1sxNV1dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE2XV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbMTddXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1sxOF1dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE5XV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbMjBdXSwgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1syMV1dLCBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzIyXV0sIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbMjNdXSwgbmNvbCA9IDgpCgpjb21wb3NpdGVfcGxvdApgYGAKCnNhdmUKYGBge3J9Cmdnc2F2ZSgiLi4vaW1hZ2VzX3RvX2V4cG9ydC9jbHVzdGVyc19jb21wb3NpdGVfcGxvdC5wbmciLCBwbG90ID0gY29tcG9zaXRlX3Bsb3QsIGRldmljZSA9ICJwbmciLCBwYXRoID0gTlVMTCwgc2NhbGUgPSAxLCB3aWR0aCA9IDIxLCBoZWlnaHQgPSAxMCwgdW5pdHMgPSAiY20iLCBkcGkgPSAzMDAsIGxpbWl0c2l6ZSA9IFRSVUUpCmBgYAoKIyA5LiBTdWJzZXQgc2V4dWFsIGNlbGxzIHsudGFic2V0fQoKTWFrZSBhIHN1YnNldHRlZCBTZXVyYXQgb2JqZWN0IG9mIHNleHVhbCBjZWxscy4gCgpJbmNsdWRlIHRoZSBwcmUtYnJhbmNoIHRvbyBhcyB3ZWxsIGFzIGFueSB3ZWlyZCBjbHVzdGVycyB0aGF0IG1heSBoYXZlIGNsdXN0ZXJlZCBvdXQuIAoKaXQncyBiZWVuIGEgd2hpbGUgc2luY2Ugd2UgbG9va2VkIGF0IHRoZSBjbHVzdGVycyBzbyBsZXQncyBjaGVjayB0aGVtIG91dCBhZ2FpbjoKYGBge3IsIGZpZy5oZWlnaHQgPSAxMCwgZmlnLndpZHRoID0gMTB9CiMjIFBsb3QKRGltUGxvdCh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLCBsYWJlbCA9IFRSVUUsIHJlcGVsID0gRkFMU0UsIHB0LnNpemUgPSAwLjA1LCBncm91cC5ieSA9ICJzZXVyYXRfY2x1c3RlcnMiLCBkaW1zID0gYygxLDIpLCByZWR1Y3Rpb24gPSAidW1hcCIpICsgY29vcmRfZml4ZWQoKQoKIyMgcGxvdApsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzFdXSArIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbMl1dICsgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1szXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzRdXSArIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbNV1dICsgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1s2XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzddXSArIGxpc3RfVU1BUHNfYnlfY2x1c3RlcltbOF1dICsgbGlzdF9VTUFQc19ieV9jbHVzdGVyW1s5XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzEwXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzExXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzEyXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzEzXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE0XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE1XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE2XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE3XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE4XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzE5XV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzIwXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzIxXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzIyXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzIzXV0gKyBsaXN0X1VNQVBzX2J5X2NsdXN0ZXJbWzI0XV0KYGBgCgojIyMgRGVmaW5lIGNlbGxzIGFuZCBzdWJzZXQKYGBge3J9CmFzZXh1YWxfZWFybHlfY2x1c3RlcnMgPC0gYyg5LCA0LCAxNSwgOCwgMSwgMTQsIDIsIDEwLCAzLCAwLCA2LCA1KQphc2V4dWFsX2xhdGVfY2x1c3RlcnMgPC0gYyg3LCAxMiwgMTgsIDIwLCAyMykKYmlwb3RlbnRpb25hbF9lYXJseV9jbHVzdGVycyA8LSAjIDA/CmJpcG90ZW50aWFsX2NsdXN0ZXJzIDwtIGMoMTEpIAptYWxlX2NsdXN0ZXJzIDwtIGMoMTYsIDEzKQpmZW1hbGVfY2x1c3RlcnMgPC0gYygyMSwgMjIsIDE3LCAxOSkKCiMjIGRlZmluZSBjZWxscwpjZWxsX25hbWVzX3N1YnNldF9tb25vY2xlX2lkcyA8LSByb3duYW1lcyh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkQG1ldGEuZGF0YVt0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkQG1ldGEuZGF0YSRtb25vY2xlX3NleCAlaW4lIGMoIkFzZXh1YWxfRWFybHkiLCAiQmlwb3RlbnRpYWwiLCAiTWFsZSIsICJGZW1hbGUiKSwgXSkKCiMjIDMsIDAsIDYsIDUgYXJlIGVhcmx5IGNsdXN0ZXJzIG9mIGFzZXh1YWxzIGJlZm9yZSB0aGUgYnJhbmNoCmNlbGxfbmFtZXNfc3Vic2V0X2NsdXN0ZXJfaWRzIDwtIHJvd25hbWVzKHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhW3RlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhJHNldXJhdF9jbHVzdGVycyAlaW4lIGMobWFsZV9jbHVzdGVycywgZmVtYWxlX2NsdXN0ZXJzLCBiaXBvdGVudGlhbF9jbHVzdGVycywgMywgMCwgNiwgNSksIF0pCgpjZWxsX25hbWVzX3N1YnNldF9pbnRlcnNlY3QgPC0gaW50ZXJzZWN0KGNlbGxfbmFtZXNfc3Vic2V0X21vbm9jbGVfaWRzLCBjZWxsX25hbWVzX3N1YnNldF9jbHVzdGVyX2lkcykKCiMjIHN1YnNldCBjZWxscyBpbnRvIG5ldyBvYmplY3QKdGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXggPC0gc3Vic2V0KHRlbnguanVzdHd0LmludGVncmF0ZWQsIGNlbGxzID0gY2VsbF9uYW1lc19zdWJzZXRfaW50ZXJzZWN0KQpgYGAKCiMjIyBpbnNwZWN0L2NoZWNrCmBgYHtyfQojIyBpbnNwZWN0IG9iamVjdAp0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleAoKIyMgbG9vayBhdCBvcmlnaW5hbCBVTUFQCkRpbVBsb3QodGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXgsIGxhYmVsID0gVFJVRSwgcmVwZWwgPSBUUlVFLCBwdC5zaXplID0gMC4xLCBzcGxpdC5ieSA9ICJleHBlcmltZW50IiwgZGltcyA9IGMoMSwyKSwgcmVkdWN0aW9uID0gInVtYXAiKSArIGNvb3JkX2ZpeGVkKCkKYGBgCgojIyMgUmVtb3ZlIGNvbnRhbWluYW50IGFzZXh1YWwgY2VsbHMKCndlIHdhbnQgdG8gcmVtb3ZlOgpgYGB7cn0KIyMgbG9vayBhdCBvcmlnaW5hbCBVTUFQCnBsb3Rfc2V4dWFsX3N1YnNldHRpbmcgPC0gRGltUGxvdCh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleCwgbGFiZWwgPSBUUlVFLCByZXBlbCA9IFRSVUUsIHB0LnNpemUgPSAwLjEsIGRpbXMgPSBjKDEsMiksIHJlZHVjdGlvbiA9ICJ1bWFwIikgKyAKICBjb29yZF9maXhlZCgpICsgCiAgZ2VvbV92bGluZShhZXMoeGludGVyY2VwdCA9IC0xLCBhbHBoYSA9IDUpKQoKcGxvdF9zZXh1YWxfc3Vic2V0dGluZwpgYGAKCmBgYHtyfQojIyBleHRyYWN0IGNlbGwgZW1iZWRkaW5ncwpkZl9zZXhfY2VsbF9lbWJlZGRpbmdzIDwtIGFzLmRhdGEuZnJhbWUodGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXhAcmVkdWN0aW9uc1tbInVtYXAiXV1AY2VsbC5lbWJlZGRpbmdzKQoKIyMgc3Vic2V0IGFueXRoaW5nIGxvd2VyIHRoYW4gLTAuOCBpbiBVTUFQIDIgYW5kIC0wLjEgaW4gVU1BUCAxCnJlbW92ZV9jZWxscyA8LSByb3cubmFtZXMoZGZfc2V4X2NlbGxfZW1iZWRkaW5nc1t3aGljaChkZl9zZXhfY2VsbF9lbWJlZGRpbmdzJFVNQVBfMSA8IC0xKSwgXSkKCiMjIHBsb3QgdGhlc2UgY2VsbHMKRGltUGxvdCh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleCwgbGFiZWwgPSBGQUxTRSwgcmVwZWwgPSBUUlVFLCBwdC5zaXplID0gMC4xLCBjZWxscy5oaWdobGlnaHQgPSByZW1vdmVfY2VsbHMsIGRpbXMgPSBjKDEsMiksIHJlZHVjdGlvbiA9ICJ1bWFwIikgKyAKICBjb29yZF9maXhlZCgpICsgCiAgc2NhbGVfY29sb3JfbWFudWFsKHZhbHVlcz1jKCIjMDAwMDAwIiwgIiNmNTRlMWUiKSkgKyAKICB0aGVtZV92b2lkKCkgKyAKICBsYWJzKHRpdGxlID0gcGFzdGUoImNlbGxzIGhpZ2hsaWdodGVkIHdpbGwgYmUgcmVtb3ZlZCIpKSArIAogIHRoZW1lKHBsb3QudGl0bGUgPSBlbGVtZW50X3RleHQoaGp1c3QgPSAwLjUpLCBsZWdlbmQucG9zaXRpb24gPSAibm9uZSIpCmBgYAoKIyMgRmluYWwgU3Vic2V0CmBgYHtyfQojIyBtYWtlIGtlZXAgY2VsbHMgZnJvbSB0aGUgcmVtb3ZlX2NlbGxzCiMjIG1ha2UgdGhlIG5vdCBpbiBmdW5jdGlvbgonJW5pJScgPC0gTmVnYXRlKCclaW4lJykKa2VlcF9jZWxscyA8LSBjb2xuYW1lcyh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleClbd2hpY2goY29sbmFtZXModGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXgpICVuaSUgcmVtb3ZlX2NlbGxzKV0KCiMjIHN1YnNldAp0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleCA8LSBzdWJzZXQodGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXgsIGNlbGxzID0ga2VlcF9jZWxscykKCiMjIGluc3BlY3QKdGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXgKYGBgCgpjb3B5IG9sZCBjbHVzdGVycyBvdmVyCmBgYHtyfQojIyBjb3B5IG9sZCBjbHVzdGVycwp0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleCA8LSBBZGRNZXRhRGF0YSh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLnNleCwgdGVueC5qdXN0d3QuaW50ZWdyYXRlZC5zZXhAbWV0YS5kYXRhJHNldXJhdF9jbHVzdGVycywgY29sLm5hbWUgPSAicG9zdF9pbnRlZ3JhdGlvbl9jbHVzdGVycyIpCmBgYAoKIyAxMC4gU2V4IGFzc2lnbm1lbnQgb2YgbXV0YW50cyAKCiMjIEEuIFNldXJhdCBNZXRob2QKCmBgYHtyfQojIyBmaW5kIHRyYW5zZmVyIGFuY2hvcnMKRGVmYXVsdEFzc2F5KHRlbnguanVzdHd0LmludGVncmF0ZWQpIDwtICJpbnRlZ3JhdGVkIgptZXJnZS5hbmNob3JzIDwtIEZpbmRUcmFuc2ZlckFuY2hvcnMocmVmZXJlbmNlID0gdGVueC5qdXN0d3QuaW50ZWdyYXRlZCwgcXVlcnkgPSBHQ1NLT19tdXRhbnRzLCAKICAgIGRpbXMgPSAxOjMwKQoKIyMgdHJhbnNmZXIgZGF0YSBiZXR3ZWVuIHJlZiBhbmQgcXVlcnkKcHJlZGljdGlvbnMgPC0gVHJhbnNmZXJEYXRhKGFuY2hvcnNldCA9IG1lcmdlLmFuY2hvcnMsIHJlZmRhdGEgPSB0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkJHNldXJhdF9jbHVzdGVyc19kb3RfcGxvdHRpbmcsIAogICAgZGltcyA9IDE6MzApCgojIyBhZGQgbWV0YSBkYXRhIHRvIG9iamVjdApHQ1NLT19tdXRhbnRzIDwtIEFkZE1ldGFEYXRhKEdDU0tPX211dGFudHMsIG1ldGFkYXRhID0gcHJlZGljdGlvbnMpCgojIyBuZXcgb2JqZWN0IGZyb20gdGhpcwptdXRhbnRfc2V1cmF0IDwtIEdDU0tPX211dGFudHMKCiMjIGxvb2sgYXQgYnJlYWtkb3duIG9mIG11dGFudCBieSBkZXNpZ25hdGlvbgpkZiA8LSBhcy5kYXRhLmZyYW1lKG11dGFudF9zZXVyYXRAbWV0YS5kYXRhKQp0YWJsZShkZiRwcmVkaWN0ZWQuaWQsIGRmJGlkZW50aXR5X25hbWVfdXBkYXRlZCkKYGBgCgpgYGB7cn0KIyMgc2VlIGhvdyB0aGlzIG92ZXJsYXBzIHdpdGggZmx1b3Jlc2NlbmNlIHNvcnRlZCBvbiBmb3IgODIwIHRvIGNvbmZpcm0gYWNjdXJhY3kKZGZfODIwIDwtIGRmW2RmJGdlbmV0aWNfYmFja2dyb3VuZCA9PSJQQkFOS0FfODIwIiwgXQp0YWJsZShkZl84MjAkcHJlZGljdGVkLmlkLCBkZl84MjAkZmx1b3Jlc2VuY2Vfc29ydGVkX29uKQpgYGAKCmluc3BlY3QgZ2QxCmBgYHtyfQpkZlt3aGljaChkZiRwcmVkaWN0ZWQuaWQgPT0gIkZlbWFsZV8xIiAmIGRmJGlkZW50aXR5X25hbWVfdXBkYXRlZCA9PSAiZ2QxIiksIF1bLCAxMTg6MTQ2XQpgYGAKCgpDYWxjdWxhdGUgc2V4IHJhdGlvcwpgYGB7cn0KIyMgdXNlIGRlc2lnbmF0aW9ucyBhYm92ZSBmb3Igc2V4ZXMKbWFsZV9jZWxscyA8LSByb3duYW1lcyhtdXRhbnRfc2V1cmF0QG1ldGEuZGF0YVttdXRhbnRfc2V1cmF0QG1ldGEuZGF0YSRwcmVkaWN0ZWQuaWQgPT0gIk1hbGUiLCBdKQpmZW1hbGVfY2VsbHMgPC0gcm93bmFtZXMobXV0YW50X3NldXJhdEBtZXRhLmRhdGFbbXV0YW50X3NldXJhdEBtZXRhLmRhdGEkcHJlZGljdGVkLmlkID09ICJGZW1hbGUiLCBdKQpzczJfbXV0YW50c19maW5hbF9tYWxlIDwtIHN1YnNldChtdXRhbnRfc2V1cmF0LCBjZWxscyA9IG1hbGVfY2VsbHMpCnNzMl9tdXRhbnRzX2ZpbmFsX2ZlbWFsZSA8LSBzdWJzZXQobXV0YW50X3NldXJhdCwgY2VsbHMgPSBmZW1hbGVfY2VsbHMpCgojIyBpbnNwZWN0CnNzMl9tdXRhbnRzX2ZpbmFsX21hbGUKc3MyX211dGFudHNfZmluYWxfZmVtYWxlCmBgYAoKYGBge3J9CiMjIGNhbGN1bGF0ZSBzZXggcmF0aW9zCiMjc3Vic2V0IG91dCBILCBzb3J0ZWQgY2VsbHM6CmRmX21hbGUgPC0gc3MyX211dGFudHNfZmluYWxfbWFsZUBtZXRhLmRhdGFbc3MyX211dGFudHNfZmluYWxfbWFsZUBtZXRhLmRhdGEkZXhjbHVkZV9mb3Jfc2V4X3JhdGlvID09IEZBTFNFLF0KCmRpbShkZl9tYWxlKQoKZGZfZmVtYWxlIDwtIHNzMl9tdXRhbnRzX2ZpbmFsX2ZlbWFsZUBtZXRhLmRhdGFbc3MyX211dGFudHNfZmluYWxfZmVtYWxlQG1ldGEuZGF0YSRleGNsdWRlX2Zvcl9zZXhfcmF0aW8gPT0gRkFMU0UsXQoKZGltKGRmX2ZlbWFsZSkKCiMjIG1ha2UgZGF0YWZyYW1lCmRmX3NleF9yYXRpbyA8LSBtZXJnZSgKICBhcy5kYXRhLmZyYW1lKHRhYmxlKGRmX21hbGUkc3ViX25hbWVfdXBkYXRlZCkpLCAKICBhcy5kYXRhLmZyYW1lKHRhYmxlKGRmX2ZlbWFsZSRzdWJfbmFtZV91cGRhdGVkKSksIAogIGJ5ID0gIlZhcjEiLCBhbGw9VFJVRSkKCiMgb3IgdXNlIGlkZW50aXR5X3VwZGF0ZWQKCiMjIGFkZCBuYW1lcwpuYW1lcyhkZl9zZXhfcmF0aW8pIDwtIGMoImdlbm90eXBlIiwgIm1hbGUiLCAiZmVtYWxlIikKCiMjIGNoYW5nZSB0aGUgTkFzIHRvIDAKZGZfc2V4X3JhdGlvW2lzLm5hKGRmX3NleF9yYXRpbyldIDwtIDAKCiMjIGNvbGxhcHNlIDgyMCB3aWxkLXR5cGVzIHRvZ2V0aGVyCmNvbWJpbmVkX20gPC0gZGZfc2V4X3JhdGlvW2RmX3NleF9yYXRpbyRnZW5vdHlwZSA9PSAiV1QtODIwXzNfNSIsIF0kbWFsZSArIGRmX3NleF9yYXRpb1tkZl9zZXhfcmF0aW8kZ2Vub3R5cGUgPT0gIldULTgyMCIsIF0kbWFsZQpjb21iaW5lZF9mIDwtIGRmX3NleF9yYXRpb1tkZl9zZXhfcmF0aW8kZ2Vub3R5cGUgPT0gIldULTgyMF8zXzUiLCBdJGZlbWFsZSArIGRmX3NleF9yYXRpb1tkZl9zZXhfcmF0aW8kZ2Vub3R5cGUgPT0gIldULTgyMCIsIF0kZmVtYWxlCmRmX3NleF9yYXRpbyA8LSByYmluZChkZl9zZXhfcmF0aW8sIGMoIldULTgyMC1jb21iaW5lZCIsIGNvbWJpbmVkX20sIGNvbWJpbmVkX2YpKQojIHJlbW92ZSBvbGQgcm93cwpkZl9zZXhfcmF0aW8gPC0gZGZfc2V4X3JhdGlvWy13aGljaChkZl9zZXhfcmF0aW8kZ2Vub3R5cGUgPT0gIldULTgyMF8zXzUiIHwgZGZfc2V4X3JhdGlvJGdlbm90eXBlID09ICJXVC04MjAiKSwgXQojIG5lZWQgdG8gbWFrZSBudW1lcmljIGFnYWluCmRmX3NleF9yYXRpbyRtYWxlIDwtIGFzLm51bWVyaWMoZGZfc2V4X3JhdGlvJG1hbGUpCmRmX3NleF9yYXRpbyRmZW1hbGUgPC0gYXMubnVtZXJpYyhkZl9zZXhfcmF0aW8kZmVtYWxlKQpkZl9zZXhfcmF0aW8kZ2Vub3R5cGUgPC0gYXMuY2hhcmFjdGVyKGRmX3NleF9yYXRpbyRnZW5vdHlwZSkKIyBhZGQgbmFtZSBmb3IgV1QgY29tYmluZWQKZGZfc2V4X3JhdGlvJGdlbm90eXBlWzE3XSA8LSAiV1QtODIwLWNvbWJpbmVkIgoKIyMgY2FsY3VsYXRlIHNleCByYXRpbwpkZl9zZXhfcmF0aW8kc2V4X3JhdGlvIDwtIChkZl9zZXhfcmF0aW8kbWFsZSArIDAuMSkvKGRmX3NleF9yYXRpbyRmZW1hbGUgKyAwLjEpCgojIyBsb2cgc2V4IHJhdGlvCmRmX3NleF9yYXRpbyRzZXhfcmF0aW9fbG9nIDwtIGxvZzEwKGRmX3NleF9yYXRpbyRzZXhfcmF0aW8pCgojI3ZpZXcKZGZfc2V4X3JhdGlvCmBgYAoKYGBge3J9CiMjIHJlbW92ZSBXVC0yIGJlY2F1c2UgaXQgaXMgcmVhbGx5IGluYXBwcm9wcmlhdGUgdG8gaGF2ZSBhIHNleCByYXRpbyBmb3IgdGhpczoKZGZfc2V4X3JhdGlvIDwtIGRmX3NleF9yYXRpb1std2hpY2goZGZfc2V4X3JhdGlvJGdlbm90eXBlID09ICJXVC1tZDUiKSxdCmBgYAoKcGxvdApgYGB7ciwgZmlnLmhlaWdodCA9IDcsIGZpZy53aWR0aCA9IDZ9CmxpYnJhcnkobGF0ZXgyZXhwKSAjIHNvIHlvdSBjYW4gcGxvdCB0aGUgZnJhY3Rpb24sIGl0J3Mgb24gQ1JBTiBgaW5zdGFsbC5wYWNrYWdlcygibGF0ZXgyZXhwIilgCgojIyBtYWtlIGV4dHJhIGNvbHVtbiBmb3IgcGxvdHRpbmcgYWVzdGhldGljczoKZGZfc2V4X3JhdGlvJGFib3ZlIDwtIGRmX3NleF9yYXRpbyRzZXhfcmF0aW9fbG9nID4gMAoKIyMgbWFrZSBleHRyYSBjb2x1bW4gd2l0aCByYXRpbyBpbiBpdDoKZGZfc2V4X3JhdGlvJGdlbm90eXBlX3dpdGhfbiA8LSBwYXN0ZTAoZGZfc2V4X3JhdGlvJGdlbm90eXBlLCAiICgiLCBkZl9zZXhfcmF0aW8kbWFsZSwgIi8iLCBkZl9zZXhfcmF0aW8kZmVtYWxlLCAiKSIpCgojIyByZW9yZGVyIGdlbm90eXBlIHNvIGl0IGlzIGluIHRoZSBjb3JyZWN0IG9yZGVyIGZvciBwbG90dGluZwpkZl9zZXhfcmF0aW8kZ2Vub3R5cGVfd2l0aF9uIDwtIGZhY3RvcihkZl9zZXhfcmF0aW8kZ2Vub3R5cGVfd2l0aF9uLCBsZXZlbHMgPSBkZl9zZXhfcmF0aW8kZ2Vub3R5cGVfd2l0aF9uW29yZGVyKGRmX3NleF9yYXRpbyRzZXhfcmF0aW9fbG9nKV0pCgojIyBwbG90CnNleF9yYXRpb19wbG90IDwtIGdncGxvdChkZl9zZXhfcmF0aW8sIGFlcyhzZXhfcmF0aW9fbG9nLCBnZW5vdHlwZV93aXRoX24sIGNvbG9yID0gYWJvdmUpKSArCiAgICAgICMjIGFkZCB0aGUgbGluZXMgZm9yIHRoZSBsb2xsaXBvcCBwbG90CiAgICAgIGdlb21fc2VnbWVudChhZXMoeCA9IDAsIHkgPSBnZW5vdHlwZV93aXRoX24sIHhlbmQgPSBzZXhfcmF0aW9fbG9nLCB5ZW5kID0gZ2Vub3R5cGVfd2l0aF9uKSwgY29sb3IgPSAiZ3JleTUwIikgKwogICAgICAjIyBhZGQgdGhlIHBvaW50cyBmb3IgdGhlIGxvbGxpcG9wIHBsb3QKICAgICAgZ2VvbV9wb2ludChhZXMoc2l6ZSA9IDQpKSArCiAgICAgICMjIGFkZCB0aGUgd2lsZC10eXBlIHJlY3RhbmdsZQogICAgICBhbm5vdGF0ZSgicmVjdCIsIHhtaW49IC0wLjIzMTgyNDc4LCB4bWF4ID0gMS4wMDEwNTc5NywgeW1pbj0tSW5mICwgeW1heD1JbmYsIGFscGhhPTAuNCwgY29sb3I9TkEsbGluZXR5cGUgPSAyLCBmaWxsPSIjOTk5OTk5IikgKwogICAgICAjIyBtYWtlIHByZXR0aWVyCiAgICAgIHRoZW1lX2NsYXNzaWMoKSArCiAgICAgIHRoZW1lKGxlZ2VuZC5wb3NpdGlvbiA9ICJub25lIiwgdGV4dD1lbGVtZW50X3RleHQoc2l6ZT0xNiwgZmFtaWx5PSJBcmlhbCIpKSArIAogICAgICAjIyBjaGFuZ2UgYXhpcyBsYWJlbHMKICAgICAgbGFicyh5ID0gVGVYKCIkR2Vub3R5cGVcIFxcbGVmdChcXGZyYWN7bl97bWFsZX19e25fe2ZlbWFsZX19XFxyaWdodCkkIikpICsKICAgICAgI2xhYnMoeSA9IGV4cHJlc3Npb24ocGFzdGUoIkdlbm90eXBlIiwgZ3JvdXAoIigiLCBmcmFjKHBhc3RlKCJuIG1hbGUiKSwgIm4gZmVtYWxlIiksICIpIikpKSkgKwogICAgICB4bGFiKGV4cHJlc3Npb24ocGFzdGUoIlNleCBSYXRpbyAoIiwgbG9nWzEwXSwgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBncm91cCgiKCIsCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgZnJhYyhwYXN0ZSgibiBtYWxlICsgMC4xIiksIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgcGFzdGUoIm4gZmVtYWxlICsgMC4xIikpLAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICIpIiksICIpIiApKSkgKwogICAgICAjIyBjaGFuZ2UgY29sb3VycyBvZiBsb2xsaXBvcHMKICAgICAgc2NhbGVfY29sb3VyX21hbnVhbCh2YWx1ZXMgPSBjKCIjYTUyYjFlIiwgIiMwMTZjMDAiKSkgKwogICAgICAjIyBhbm5vdGF0ZSBwaGVub3R5cGVzCiAgICAgIGdlb21faGxpbmUoYWVzKHlpbnRlcmNlcHQgPSA1LjUpKSAjKwogICAgICAjZ2VvbV9obGluZShhZXMoeWludGVyY2VwdCA9IDE0LjUpKQoKcHJpbnQoc2V4X3JhdGlvX3Bsb3QpCgojIyMgQ0hBTkdFIFRPIEJJRyBCUkFDS0VUIEhFUkU6IGh0dHBzOi8vd3d3Lm92ZXJsZWFmLmNvbS9sZWFybi9sYXRleC9CcmFja2V0c19hbmRfUGFyZW50aGVzZXMgCgojcGFzdGUoIlNleCBSYXRpbyIsICJcbiIsICJsb2cxMCgobiBtYWxlICsgMC4xKS8obiBmZW1hbGUgKyAwLjEpKSIpCiN0aGVtZShsZWdlbmQucG9zaXRpb24gPSAibm9uZSIsIHRleHQ9ZWxlbWVudF90ZXh0KHNpemU9MTYsIGZhbWlseT0iQXJpYWwiKSkKCiMjIGZyYWN0aW9uIHRpdGxlczogaHR0cHM6Ly9ncm91cHMuZ29vZ2xlLmNvbS9mb3J1bS8jIXRvcGljL2dncGxvdDIvYmdOUm5aODJoSlkgCiMjIGh0dHBzOi8vdWMtci5naXRodWIuaW8vbG9sbGlwb3AKCiNnZ3NhdmUoZmlsZW5hbWUgPSAiLi4vaW1hZ2VzX3RvX2V4cG9ydC9zZXhfcmF0aW9fcGxvdC5wbmciLCBkZXZpY2UgPSAicG5nIiwgd2lkdGggPSA3LCBoZWlnaHQgPSA3LCB1bml0cyA9ICJpbiIpCmBgYAoKIyMgQi4gU0NNQVAgTWV0aG9kCgojIyMgQnVpbGQgdGhlIGluZGV4CgpgYGB7cn0KIyMjIE1ha2luZyBhbiBvcnRob2xvZyByZWZlcmVuY2UgaW5kZXgKCiMjIGxvYWQgaW4gbWNhIGRhdGEKI2NvdW50cyA8LSByZWFkLmNzdigiLi4vc2NtYXAvYWxscGIxMHhfY291bnRzLmNzdiIsIHJvdy5uYW1lcyA9IDEpCiNwaGVubyA8LSByZWFkLmNzdigiLi4vc2NtYXAvYWxscGIxMHhfcGhlbm8uY3N2IikKI2dncGxvdChwaGVubywgYWVzKHg9UEMyXzNkLCB5ID0gUEMzXzNkLGNvbG91cj1hYnNjbHVzdDMpKSArIGdlb21fcG9pbnQoKQoKIyMgbG9hZCByZXF1aXJlZCBsaWJyYXJpZXMKbGlicmFyeShzY21hcCkgI2h0dHBzOi8vYmlvY29uZHVjdG9yLm9yZy9wYWNrYWdlcy9yZWxlYXNlL2Jpb2MvaHRtbC9zY21hcC5odG1sIApsaWJyYXJ5KFNpbmdsZUNlbGxFeHBlcmltZW50KSAjCgojcHJlcCB0aGUgU0NFLCBpZiB3YXMgb3JpZ2luYWxseSBhIFN1ZXJhdCBvYmplY3QgbmVlZCB0aGUgZGZzIHRvIGJlIHJlZ3VsYXIgbWF0cmljZXMKI3BiX2ZpbHRlcmVkX3NjZV9vcnRoIDwtIHBiX2ZpbHRlcmVkX3NjZV9vcnRoWywgY29sRGF0YShwYl9maWx0ZXJlZF9zY2Vfb3J0aCkkYWJzY2x1c3QzICE9ICI4Il0KI3NjZSA8LSBwYl9maWx0ZXJlZF9zY2Vfb3J0aAojcGNhIDwtIHBsb3RQQ0Eoc2NlKQojcGNzIDwtIHBjYSRkYXRhCiN0YWJsZShyb3duYW1lcyhwY3MpPT1jb2xuYW1lcyhzY2UpKQojY29sRGF0YShzY2UpIDwtIGNiaW5kKGNvbERhdGEoc2NlKSwgcGNzKQojcm93RGF0YShzY2UpJGZlYXR1cmVfc3ltYm9sIDwtIHJvd0RhdGEoc2NlKSRnZW5lCgojIyBleHRyYWN0IGRhdGEgZnJvbSBTZXVyYXQKI2NlbGxzX3RlbnggPC0gcm93bmFtZXModGVueC5qdXN0d3QuaW50ZWdyYXRlZEBtZXRhLmRhdGFbd2hpY2godGVueC5qdXN0d3QuaW50ZWdyYXRlZEBtZXRhLmRhdGEkZXhwZXJpbWVudCA9PSAidGVueF81ayIpLCBdKQojdGVueC5qdXN0d3QuaW50ZWdyYXRlZC4xMGsgPC0gc3Vic2V0KHRlbnguanVzdHd0LmludGVncmF0ZWQsIGNlbGxzID0gY2VsbHNfdGVueCkKY291bnRzID0gYXMubWF0cml4KEdldEFzc2F5RGF0YSh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLCBzbG90ID0gImNvdW50cyIsIGFzc2F5ID0gIlJOQSIpKQpwaGVubyA9IGFzLmRhdGEuZnJhbWUodGVueC5qdXN0d3QuaW50ZWdyYXRlZEBtZXRhLmRhdGEpCgojIyBhZGQgVU1BUCBjb29yZGluYXRlcwpkZl9zZXhfY2VsbF9lbWJlZGRpbmdzIDwtIGFzLmRhdGEuZnJhbWUodGVueC5qdXN0d3QuaW50ZWdyYXRlZEByZWR1Y3Rpb25zW1sidW1hcCJdXUBjZWxsLmVtYmVkZGluZ3MpCnBoZW5vIDwtIGNiaW5kKHBoZW5vLCBkZl9zZXhfY2VsbF9lbWJlZGRpbmdzKQoKIyMgU2V0IHVwIG9iamVjdApzY2UgPC0gU2luZ2xlQ2VsbEV4cGVyaW1lbnQobGlzdChjb3VudHM9Y291bnRzKSwKICAgIGNvbERhdGE9RGF0YUZyYW1lKGxhYmVsPXBoZW5vKSwKICAgIHJvd0RhdGE9RGF0YUZyYW1lKGZlYXR1cmVfc3ltYm9sPXJvd25hbWVzKGNvdW50cykpKQpzY2UKCiMjIG1hbnVhbCBsb2dnaW5nCiNjb3VudHNfMSA8LSBhc3NheShzY2UsICJjb3VudHMiKQojbGlic2l6ZXMgPC0gY29sU3Vtcyhjb3VudHNfMSkKI3NpemUuZmFjdG9ycyA8LSBsaWJzaXplcy9tZWFuKGxpYnNpemVzKQojbG9nY291bnRzKHNjZSkgPC0gbG9nMih0KHQoY291bnRzXzEpL3NpemUuZmFjdG9ycykgKyAxKQojY291bnRzKHNjZSkgPC0gYXMubWF0cml4KGNvdW50cyhzY2UpKQojbG9nY291bnRzKHNjZSkgPC0gYXMubWF0cml4KGxvZ2NvdW50cyhzY2UpKQpsb2djb3VudHMoc2NlKSA8LSBhcy5tYXRyaXgoR2V0QXNzYXlEYXRhKHRlbnguanVzdHd0LmludGVncmF0ZWQsIHNsb3QgPSAiZGF0YSIsIGFzc2F5ID0gIlJOQSIpKQoKIyMgcmVtb3ZlIGZlYXR1cmVzIHdpdGggZHVwbGljYXRlZCBuYW1lcwpzY2UgPC0gc2NlWyFkdXBsaWNhdGVkKHJvd25hbWVzKHNjZSkpLCBdCgojIyBidWlsZCBzY21hcC1jZWxsIHJlZmVyZW5jZSBpbmRleCwgc2F2ZSB0aGlzIHJkcwpzY2UgPC0gc2VsZWN0RmVhdHVyZXMoc2NlLCBzdXBwcmVzc19wbG90ID0gRkFMU0UsIG5fZmVhdHVyZXMgPSAyMDAwKQp0YWJsZShyb3dEYXRhKHNjZSkkc2NtYXBfZmVhdHVyZXMpCnNldC5zZWVkKDEpCnNjZSA8LSBpbmRleENlbGwoc2NlLCBNID0gNTAsIGsgPSA4MCkKbmFtZXMobWV0YWRhdGEoc2NlKSRzY21hcF9jZWxsX2luZGV4KQojbGVuZ3RoKG1ldGFkYXRhKHNjZSkkc2NtYXBfY2VsbF9pbmRleCRzdWJjZW50cm9pZHMpCiNkaW0obWV0YWRhdGEoc2NlKSRzY21hcF9jZWxsX2luZGV4JHN1YmNlbnRyb2lkc1tbMV1dKQojbWV0YWRhdGEoc2NlKSRzY21hcF9jZWxsX2luZGV4JHN1YmNlbnRyb2lkc1tbMV1dWywxOjVdCiNkaW0obWV0YWRhdGEoc2NlKSRzY21hcF9jZWxsX2luZGV4JHN1YmNsdXN0ZXJzKQoKI3NhdmVSRFMocGJfZmlsdGVyZWRfc2NlX29ydGgsIGZpbGU9InBiX2ZpbHRlcmVkX3NjZV9vcnRoaW5kZXhfMjAxODExMDkucmRzIikKYGBgCgojIyMgTWFwIHRvIHRoZSBpbmRleAoKYGBge3J9CiNyb3dEYXRhKHBiX2ZpbHRlcmVkX3NjZV9vcnRoKSRmZWF0dXJlX3N5bWJvbCA8LSByb3dEYXRhKHBiX2ZpbHRlcmVkX3NjZV9vcnRoKSRvcnRoX25hbWUKI3Jvd25hbWVzKHBiX2ZpbHRlcmVkX3NjZV9vcnRoKSA8LSByb3dEYXRhKHBiX2ZpbHRlcmVkX3NjZV9vcnRoKSRvcnRoX25hbWUKCiNwcmVwIHRoZSBTQ0UsIGlmIHdhcyBvcmlnaW5hbGx5IGEgU3VlcmF0IG9iamVjdCBuZWVkIHRoZSBkZnMgdG8gYmUgcmVndWxhciBtYXRyaWNlcwojcGJfZmlsdGVyZWRfc2NlX29ydGggPC0gcGJfZmlsdGVyZWRfc2NlX29ydGhbLCBjb2xEYXRhKHBiX2ZpbHRlcmVkX3NjZV9vcnRoKSRhYnNjbHVzdDMgIT0gIjgiXQojc2NlIDwtIHBiX2ZpbHRlcmVkX3NjZV9vcnRoCiNwY2EgPC0gcGxvdFBDQShzY2UpCiNwY3MgPC0gcGNhJGRhdGEKI3RhYmxlKHJvd25hbWVzKHBjcyk9PWNvbG5hbWVzKHNjZSkpCgojY29sRGF0YShzY2UpIDwtIGNiaW5kKGNvbERhdGEoc2NlKSwgcGNzKQoKbXV0YW50c19jb3VudHMgPSBhcy5tYXRyaXgoR2V0QXNzYXlEYXRhKG11dGFudF9zZXVyYXQsIHNsb3QgPSAiY291bnRzIiwgYXNzYXkgPSAiUk5BIikpCm11dGFudHNfcGhlbm8gPSBhcy5kYXRhLmZyYW1lKG11dGFudF9zZXVyYXRAbWV0YS5kYXRhKQoKI3Jvd0RhdGEoc2NlKSRmZWF0dXJlX3N5bWJvbCA8LSByb3dEYXRhKHNjZSkkZ2VuZQptdXRhbnRzLnNjZSA8LSBTaW5nbGVDZWxsRXhwZXJpbWVudChsaXN0KGNvdW50cz1tdXRhbnRzX2NvdW50cyksCiAgICBjb2xEYXRhPURhdGFGcmFtZShsYWJlbD1tdXRhbnRzX3BoZW5vKSwKICAgIHJvd0RhdGE9RGF0YUZyYW1lKGZlYXR1cmVfc3ltYm9sPXJvd25hbWVzKG11dGFudHNfY291bnRzKSkpCm11dGFudHMuc2NlCgojIyBhZGQgbG9nIGNvdW50cwojIyBtYW51YWwKI2NvdW50c18xIDwtIGFzc2F5KG11dGFudHMuc2NlLCAiY291bnRzIikKI2xpYnNpemVzIDwtIGNvbFN1bXMoY291bnRzXzEpCiNzaXplLmZhY3RvcnMgPC0gbGlic2l6ZXMvbWVhbihsaWJzaXplcykKI2xvZ2NvdW50cyhtdXRhbnRzLnNjZSkgPC0gbG9nMih0KHQoY291bnRzXzEpL3NpemUuZmFjdG9ycykgKyAxKQojIyBmcm9tIHNldXJhdApsb2djb3VudHMobXV0YW50cy5zY2UpIDwtIGFzLm1hdHJpeChHZXRBc3NheURhdGEobXV0YW50X3NldXJhdCwgc2xvdCA9ICJkYXRhIiwgYXNzYXkgPSAiUk5BIikpCgojUHJvamVjdCBxdWVyeSBkYXRhIHNldCBvbnRvIGNlbGwgaW5kZXgKc2NtYXBDZWxsX3Jlc3VsdHMgPC0gc2NtYXBDZWxsKAogIG11dGFudHMuc2NlLCAKICBsaXN0KHd0ID0gbWV0YWRhdGEoc2NlKSRzY21hcF9jZWxsX2luZGV4CiAgKQopCgojI0xvb2sgaW50byB0aGUgcmVzdWx0cwojIEZvciBlYWNoIGRhdGFzZXQgdGhlcmUgYXJlIHR3byBtYXRyaWNpZXMuIGNlbGxzIG1hdHJpeCBjb250YWlucyB0aGUgdG9wIDEwIChzY21hcCBkZWZhdWx0KSBjZWxsIElEcyBvZiB0aGUgY2VsbHMgb2YgdGhlIHJlZmVyZW5jZSBkYXRhc2V0IHRoYXQgYSBnaXZlbiBjZWxsIG9mIHRoZSBwcm9qZWN0aW9uIGRhdGFzZXQgaXMgY2xvc2VzdCB0bzoKIyAgIAojICAgR2l2ZSBhc3NpZ25tZW50cyBpbiB0d28gd2F5czoKIyAgIDEuIFRha2UgdGhlIHRvcCBjZWxsIGFzc2lnbm1lbnQgYWJzIGNsdXN0LCBpZiBjb3NpbmUgc2ltaWxhcml0eSBpcyBsZXNzIHRoYW4gMC40IChvciBhZGp1c3QgaWYgbmVlZGVkKSBtYXJrIGFzIHVuYXNzaWduZWQKIyAyLiBGb3IgdGhlIHRvcCAzIG5lYXJlc3QgbmVpZ2hib3JzLCBnZXQgYSBtZWFuIG9mIHRoZSBQQ0EgY29vcmRpbmF0ZXMgYW5kIHNuYXAgdG8gdGhlIG5lYXJlc3QgY2VsbCBvZiB0aG9zZSBjb29yZGluYXRlcy4gSWYgYW55IG9mIHRoZSB0b3AgdGhyZWUgY2VsbHMgYXJlIHNpbSBiZWxvdyAwLjQgdGhlbiBtYXJrIGFzIHVuYXNzaWduZWQuCgoKIyNUb3AgY2VsbCBhc3NpZ25tZW50IG1ldGhvZAoKIyMgbG9vayBhdCBhc3NpZ25tZW50cwpzY21hcENlbGxfcmVzdWx0cyR3dCRjZWxsc1ssIDE6M10KIyMgZ2V0IGNlbGwgaW5kZXhlcyAKZ2V0Y2VsbHMgPC0gc2NtYXBDZWxsX3Jlc3VsdHMkd3QkY2VsbHNbMSwgXQojIyBleGN0cmFjdCBjZWxscyBtZXRhZGF0YSBmcm9tIGluZGV4CmNkc2NlIDwtIGNvbERhdGEoc2NlKVtnZXRjZWxscywgXQojIyBleHRyYWN0IHNpbWlsYXJpdGllcwp0b3BzaW0gPC0gc2NtYXBDZWxsX3Jlc3VsdHMkd3Qkc2ltaWxhcml0aWVzWzEsIF0KIyMgZ2V0IG5hbWUgb2YgdG9wIGNlbGwgbWF0Y2hlZAptdXRhbnRzLnNjZSR0b3BjZWxsIDwtIHJvd25hbWVzKGNkc2NlKQojIyBnZXQgc2V4IGlkCm11dGFudHMuc2NlJHRvcGNlbGxfYWMgPC0gY2RzY2UkbGFiZWwubW9ub2NsZV9zZXgKIyMgZ2V0IGNvb3JkaW5hdGVzIG9mIG1hdGNoZWQgY2VsbAptdXRhbnRzLnNjZSRpbmRleFBDMSA8LSBjZHNjZSRsYWJlbC5VTUFQXzEKbXV0YW50cy5zY2UkaW5kZXhQQzIgPC0gY2RzY2UkbGFiZWwuVU1BUF8yCiNtdXRhbnRzLnNjZSRwYnB0IDwtIGNkc2NlJHBzZXVkb3RpbWUKI211dGFudHMuc2NlJHBiYnVsayA8LSBjZHNjZSRidWxrCm11dGFudHMuc2NlJHRvcGNlbGxfc3AgPC0gbXV0YW50cy5zY2UkdG9wY2VsbF9hYwptdXRhbnRzLnNjZSR0b3BzaW0gPC0gdG9wc2ltCm11dGFudHMuc2NlJHRvcGNlbGxfc3BbbXV0YW50cy5zY2UkdG9wc2ltIDwgMC40XSA8LSAidW5hc3NpZ25lZCIKdGFibGUobXV0YW50cy5zY2UkdG9wY2VsbF9zcCkKYGBgCgpgYGB7cn0KIyMgc2VlIGhvdyB0aGlzIG92ZXJsYXBzIHdpdGggZmx1b3Jlc2NlbmNlIHNvcnRlZCBvbiBmb3IgODIwIHRvIGNvbmZpcm0gYWNjdXJhY3kKZGZfODIwIDwtIGNvbERhdGEobXV0YW50cy5zY2UpW2NvbERhdGEobXV0YW50cy5zY2UpJGxhYmVsLmdlbmV0aWNfYmFja2dyb3VuZCA9PSJQQkFOS0FfODIwIiwgXQp0YWJsZShkZl84MjAkdG9wY2VsbF9zcCwgZGZfODIwJGxhYmVsLmZsdW9yZXNlbmNlX3NvcnRlZF9vbikKYGBgCgpgYGB7cn0KdGFibGUoY29sRGF0YShtdXRhbnRzLnNjZSkkdG9wY2VsbF9zcCwgY29sRGF0YShtdXRhbnRzLnNjZSkkbGFiZWwuaWRlbnRpdHlfbmFtZV91cGRhdGVkKQpgYGAKCgpgYGB7cn0KIyMjIyBUT1AgM05OIG1ldGhvZAoKI1RoaXMgZnVuY3Rpb24gbWFrZXMgYSBsaXN0IG9mIHRoZSBQQyBtZWFucyBmb3IgZWFjaCBjZWxsIGFuZCB0aGVuIGRvLmNhbGwgYmVsb3cgcmJpbmRzIHRoZW0gaW50byBhIGRhdGFmcmFtZSBjYWxsZWQgYmlnX2RhdGEKCmRhdGFsaXN0ID0gbGlzdCgpCgpmb3IgKGkgaW4gY29sbmFtZXMoc2NtYXBDZWxsX3Jlc3VsdHMkd3QkY2VsbHMpKSB7CiAgCiAgZ2V0Y2VsbHN0ZXN0IDwtIHNjbWFwQ2VsbF9yZXN1bHRzJHd0JGNlbGxzWzE6MywgaV0KICBjZHNjZXRlc3QgPC0gY29sRGF0YShzY2UpW2dldGNlbGxzdGVzdCwgXQogIFBDMW1lYW4gPC0gbWVhbihjZHNjZXRlc3QkbGFiZWwuVU1BUF8xKQogIFBDMm1lYW4gPC0gbWVhbihjZHNjZXRlc3QkbGFiZWwuVU1BUF8yKQogICMgLi4uIG1ha2Ugc29tZSBkYXRhCiAgZGF0IDwtIGRhdGEuZnJhbWUoaSwgUEMxbWVhbiwgUEMybWVhbikKICBkYXQkaSA8LSBpICAjIG1heWJlIHlvdSB3YW50IHRvIGtlZXAgdHJhY2sgb2Ygd2hpY2ggaXRlcmF0aW9uIHByb2R1Y2VkIGl0PwogIGRhdGFsaXN0W1tpXV0gPC0gZGF0ICMgYWRkIGl0IHRvIHlvdXIgbGlzdAp9CgpiaWdfZGF0YSA9IGRvLmNhbGwocmJpbmQsIGRhdGFsaXN0KQojIG9yIGJpZ19kYXRhIDwtIGRwbHlyOjpiaW5kX3Jvd3MoZGF0YWxpc3QpCiMgb3IgYmlnX2RhdGEgPC0gZGF0YS50YWJsZTo6cmJpbmRsaXN0KGRhdGFsaXN0KQoKdGVzdCA8LSBiaWdfZGF0YVsxLCBdCgpkZiA8LSBkYXRhLmZyYW1lKFg9Y29sRGF0YShzY2UpJGxhYmVsLlVNQVBfMSwgWT1jb2xEYXRhKHNjZSkkbGFiZWwuVU1BUF8yLCByb3cubmFtZXMgPSByb3duYW1lcyhjb2xEYXRhKHNjZSkpKQoKI3RoZSBzbmFwIGZ1bmN0aW9uIHNuYXBzIHRvIHRoZSBuZWFyZXN0IGNlbGwgaW4gUEMgY29vcmRpYW50ZXMKc25hcCA8LSBmdW5jdGlvbihkZiwgdGVzdCl7CiAgcmVxdWlyZShCaW9iYXNlKQogIGQgPC0gbWF0Y2hwdChhcy5tYXRyaXgoZGYpLAogICAgICAgICAgICAgICBhcy5tYXRyaXgoZGF0YS5mcmFtZShYPXRlc3QkUEMxbWVhbixZPXRlc3QkUEMybWVhbikpKQogIAogIG1pbl9yb3cgPC0gcm93bmFtZXMoZFtkJGRpc3RhbmNlPT1taW4oZCRkaXN0YW5jZSksXSkKICAKICB0ZXN0JFhfc25hcCA8LSB1bmlxdWUoZGZbbWluX3JvdywiWCJdKQogIHRlc3QkWV9zbmFwIDwtIHVuaXF1ZShkZlttaW5fcm93LCJZIl0pCiAgdGVzdCRwYl9jZWxsIDwtIG1pbl9yb3cKICAKICB0ZXN0Cn0KCiN0aGlzIGxvb3BzIHRocm91Z2ggZWFjaCBjZWxsIGFuZCBpbiBiaWdfZGF0YSBhbmQgcnVucyB0aGUgc25hcCBmdW5jdGlvbgpkYXRhbGlzdDIgPSBsaXN0KCkKY29sbmFtZXMoYmlnX2RhdGEpIDwtIGMoInNhbXBsZV9pZCIsICJQQzFtZWFuIiwgIlBDMm1lYW4iKQpmb3IgKGkgaW4gcm93bmFtZXMoYmlnX2RhdGEpKSB7CiAgdGVzdCA8LSBiaWdfZGF0YVtpLCBdCiAgY29vcmQgPC0gc25hcChkZiwgdGVzdCkKICBjb29yZCRpIDwtIGkKICBkYXRhbGlzdDJbW2ldXSA8LSBjb29yZAp9CmJpZ19kYXRhMiA9IGRvLmNhbGwocmJpbmQsIGRhdGFsaXN0MikKCgp0YWJsZShyb3duYW1lcyhiaWdfZGF0YTIpPT1yb3duYW1lcyhjb2xEYXRhKG11dGFudHMuc2NlKSkpCgphbGxwYmNkIDwtIGNvbERhdGEoc2NlKQphbGxwYmNkIDwtIGFzLmRhdGEuZnJhbWUoYWxscGJjZCkKcGJhYnNjbHVzdCA8LSBhbGxwYmNkWywgYygibGFiZWwucHRfaWRfY29scyIsICJsYWJlbC5pZGVudGl0eV9uYW1lX3VwZGF0ZWQiLCAibGFiZWwubW9ub2NsZV9zZXgiKSwgZHJvcD1GQUxTRV0KcGJhYnNjbHVzdCRwYl9zYW1wbGVfaWQgPC0gcm93bmFtZXMocGJhYnNjbHVzdCkKCiMgTm93IG1lcmdlIHRoZSBwYyBjZWxsIGFzaWdubWVudHMgd2l0aCB0aGVpciBhYnMgY2x1c3QgYW5kIGdldCBpbiB0aGUgcmlnaHQgb3JkZXIKYmlnX2RhdGEzIDwtIG1lcmdlKGJpZ19kYXRhMiwgcGJhYnNjbHVzdCwgYnkueCA9ICJwYl9jZWxsIiwgYnkueSA9ICJwYl9zYW1wbGVfaWQiLCBhbGwueD1UUlVFLCBhbGwueT1GQUxTRSkKYmlnX2RhdGE0IDwtIGJpZ19kYXRhM1ttYXRjaChyb3duYW1lcyhiaWdfZGF0YTIpLCBiaWdfZGF0YTMkc2FtcGxlX2lkKSwgXQoKCmNvbG9ycyA8LSBjKCI2Ij0iIzc4QzY3OSIsCiAgICAgICAgICAgICIyIj0iI0QxRUM5RiIsCiAgICAgICAgICAgICIwIj0iI0ZFQjI0QyIsCiAgICAgICAgICAgICIxIj0iI0Y0Q0Y2MyIsCiAgICAgICAgICAgICIzIj0iI0ZFRUVBQSIsCiAgICAgICAgICAgICI0Ij0iIzg1QjFEMyIsCiAgICAgICAgICAgICI3Ij0iIzllY2FlMSIsCiAgICAgICAgICAgICI1Ij0iI0M5RThGMSIsCiAgICAgICAgICAgICJNIj0gIiNCN0I3RDgiLAogICAgICAgICAgICAiRiI9IiM5Qzk2QzYiLAogICAgICAgICAgICAidW5hc3NpZ25lZCI9ImJsYWNrIikKCmdncGxvdChiaWdfZGF0YTQsIGFlcyhQQzFtZWFuLCBQQzJtZWFuKSkgKwogIGdlb21fcG9pbnQoY29sID0gYmlnX2RhdGE0JGxhYmVsLnB0X2lkX2NvbHMpICsKICAgICAgICAgICAgICAgICAgICAgdGhlbWVfdm9pZCgpICsKICAgICAgICAgICAgICAgICAgICAgY29vcmRfZml4ZWQoKQoKIysgZ2VvbV9wb2ludChhZXMoY29sb3VyPWZhY3RvcihiaWdfZGF0YTQkbGFiZWwubGFiZWwucHRfaWRfY29scykpKSArIHNjYWxlX2NvbG9yX21hbnVhbCh2YWx1ZXMgPSBjb2xvcnMpIAoKICAgICAgICAgICAgICAgICAgICAgCgpnZ3Bsb3QoYmlnX2RhdGE0LCBhZXMoWF9zbmFwLCBZX3NuYXApKSArCiAgZ2VvbV9wb2ludChjb2wgPSBiaWdfZGF0YTQkbGFiZWwucHRfaWRfY29scykgKwogICAgICAgICAgICAgICAgICAgICB0aGVtZV92b2lkKCkgKwogICAgICAgICAgICAgICAgICAgICBjb29yZF9maXhlZCgpIAoKIysgZ2VvbV9wb2ludChhZXMoY29sb3VyPWZhY3RvcihiaWdfZGF0YTQkbGFiZWwubGFiZWwucHRfaWRfY29scykpKSArIHNjYWxlX2NvbG9yX21hbnVhbCh2YWx1ZXMgPSBjb2xvcnMpIAoKIyNhZGQgaW5mbyB0byBTQ0UgYW5kIHNhdmUgY29sRGF0YSwgdG8gYmUgYW4gYXNzaWduZWQgY2VsbCBhbGwgM05OIG11c3QgaGF2ZSBhIGNvcyBzaW0gPjAuNApzY21hcENlbGxfcmVzdWx0cyR3dCRzaW1pbGFyaXRpZXNbLCAxOjNdCnRvcHNpbTEgPC0gc2NtYXBDZWxsX3Jlc3VsdHMkd3Qkc2ltaWxhcml0aWVzWzEsIF0KdG9wc2ltMiA8LSBzY21hcENlbGxfcmVzdWx0cyR3dCRzaW1pbGFyaXRpZXNbMiwgXQp0b3BzaW0zIDwtIHNjbWFwQ2VsbF9yZXN1bHRzJHd0JHNpbWlsYXJpdGllc1szLCBdCgp0YWJsZShiaWdfZGF0YTQkc2FtcGxlX2lkPT1yb3duYW1lcyhjb2xEYXRhKG11dGFudHMuc2NlKSkpCiNwZm9iaiRwYl9jZWxsIDwtIGJpZ19kYXRhNCRwYl9jZWxsCiNwZm9iaiRQQzFtZWFuIDwtIGJpZ19kYXRhNCRQQzFtZWFuCmJkNCA8LSBiaWdfZGF0YTRbLCBjKCJwYl9jZWxsIiwgInNhbXBsZV9pZCIsICJQQzFtZWFuIiwgIlBDMm1lYW4iLCAiWF9zbmFwIiwgIllfc25hcCIsICJsYWJlbC5wdF9pZF9jb2xzIiwgImxhYmVsLmlkZW50aXR5X25hbWVfdXBkYXRlZCIsICJsYWJlbC5tb25vY2xlX3NleCIpXQoKY29sRGF0YShtdXRhbnRzLnNjZSkgPC0gY2JpbmQoY29sRGF0YShtdXRhbnRzLnNjZSksIGJkNCkKCm11dGFudHMuc2NlJHRvcHNpbTEgPC0gdG9wc2ltMQptdXRhbnRzLnNjZSR0b3BzaW0yIDwtIHRvcHNpbTIKbXV0YW50cy5zY2UkdG9wc2ltMyA8LSB0b3BzaW0zCm11dGFudHMuc2NlJHN0YWdlX3ByZWQgPC0gYmlnX2RhdGE0JGxhYmVsLm1vbm9jbGVfc2V4Cm11dGFudHMuc2NlJHN0YWdlX3ByZWRbbXV0YW50cy5zY2UkdG9wc2ltMSA8IDAuNCB8IG11dGFudHMuc2NlJHRvcHNpbTIgPCAwLjQgfCBtdXRhbnRzLnNjZSR0b3BzaW0zIDwgMC40XSA8LSAidW5hc3NpZ25lZCIKdGFibGUobXV0YW50cy5zY2Ukc3RhZ2VfcHJlZCkKCiN3cml0ZS5jc3YoYmQ0LCAicGZjZWxsYXNzaWdubWVudHN3aXRobWVhbjNubl8yMDE4MTAyOS5jc3YiKQojd3JpdGUuY3N2KGNvbERhdGEocGZvYmopLCAicGYzZDcxMDBzY21hcGNsdXN0czJtZXRob2RpbmRleG4xMDBfMjAxOTAxMDcuY3N2IikKYGBgCgpgYGB7cn0KIyMgc2VlIGhvdyB0aGlzIG92ZXJsYXBzIHdpdGggZmx1b3Jlc2NlbmNlIHNvcnRlZCBvbiBmb3IgODIwIHRvIGNvbmZpcm0gYWNjdXJhY3kKZGZfODIwIDwtIGNvbERhdGEobXV0YW50cy5zY2UpW2NvbERhdGEobXV0YW50cy5zY2UpJGxhYmVsLmdlbmV0aWNfYmFja2dyb3VuZCA9PSJQQkFOS0FfODIwIiwgXQp0YWJsZShkZl84MjAkc3RhZ2VfcHJlZCwgZGZfODIwJGxhYmVsLmZsdW9yZXNlbmNlX3NvcnRlZF9vbikKYGBgCgpgYGB7cn0KdGFibGUoY29sRGF0YShtdXRhbnRzLnNjZSkkc3RhZ2VfcHJlZCwgY29sRGF0YShtdXRhbnRzLnNjZSkkbGFiZWwuaWRlbnRpdHlfbmFtZV91cGRhdGVkKQpgYGAKCgojIyBtYWtlIGZpbmFsIHBsb3Qgd2l0aCBNQ0EgZGF0YSBiZWxvdwpgYGB7cn0KIyMgUmVhZCBpbiBNQ0EgZGF0YQpwaGVubyA8LSByZWFkLmNzdigiLi4vc2NtYXAvYWxscGIxMHhfcGhlbm8uY3N2IikKIyMgZXh0cmFjdCByb3dzIG5lZWRlZCBmb3IgcGxvdHRpbmcKZGZfcGxvdCA8LSBwaGVub1ssYygiUEMyXzNkIiwgIlBDM18zZCIsICJhYnNjbHVzdDMiKV0KZGZfcGxvdCRleHBlcmltZW50IDwtIGRmX3Bsb3QkYWJzY2x1c3QzCgojIyBleHRyYWN0IHJvd3MgbmVlZGVkIGZvciBwbG90dGluZyBmcm9tIG1hcHBlZCBvYmplY3QKZGZfcGxvdF9wbSA8LSBhcy5kYXRhLmZyYW1lKGNvbERhdGEocG1fc3MyX2ZpZWxkLnNjZS5vcnRoKSkKZGZfcGxvdF9wbSA8LSBkZl9wbG90X3BtWyAsYygiWF9zbmFwIiwgIllfc25hcCIsICJsYWJlbC5hYnNjbHVzdDMiKV0KZGZfcGxvdF9wbSRleHBlcmltZW50IDwtICJwbSIKY29sbmFtZXMoZGZfcGxvdF9wbSkgPC0gYygiUEMyXzNkIiwgIlBDM18zZCIsICJhYnNjbHVzdDMiLCAiZXhwZXJpbWVudCIpCgojIyBleHRyYWN0IHJvd3MgbmVlZGVkIGZvciBwbG90dGluZyBmcm9tIG1hcHBlZCBvYmplY3QKZGZfcGxvdF9wZiA8LSBhcy5kYXRhLmZyYW1lKGNvbERhdGEocGZfc3MyX2ZpZWxkLnNjZS5vcnRoKSkKZGZfcGxvdF9wZiA8LSBkZl9wbG90X3BmWyAsYygiWF9zbmFwIiwgIllfc25hcCIsICJsYWJlbC5hYnNjbHVzdDMiKV0KZGZfcGxvdF9wZiRleHBlcmltZW50IDwtICJwZiIKY29sbmFtZXMoZGZfcGxvdF9wZikgPC0gYygiUEMyXzNkIiwgIlBDM18zZCIsICJhYnNjbHVzdDMiLCAiZXhwZXJpbWVudCIpCgojIyBiaW5kIHRvZ2V0aGVyCmRmX3Bsb3QgPC0gcmJpbmQoZGZfcGxvdCwgZGZfcGxvdF9wZiwgZGZfcGxvdF9wbSkKCmNvbG9ycyA8LSBjKCI2Ij0iIzc4QzY3OSIsCiAgICAgICAgICAgICIyIj0iI0QxRUM5RiIsCiAgICAgICAgICAgICIwIj0iI0ZFQjI0QyIsCiAgICAgICAgICAgICIxIj0iI0Y0Q0Y2MyIsCiAgICAgICAgICAgICIzIj0iI0ZFRUVBQSIsCiAgICAgICAgICAgICI0Ij0iIzg1QjFEMyIsCiAgICAgICAgICAgICI3Ij0iIzllY2FlMSIsCiAgICAgICAgICAgICI1Ij0iI0M5RThGMSIsCiAgICAgICAgICAgICJNIj0gIiNCN0I3RDgiLAogICAgICAgICAgICAiRiI9IiM5Qzk2QzYiLAogICAgICAgICAgICAidW5hc3NpZ25lZCI9ImJsYWNrIiwKICAgICAgICAgICAgInBtIiA9ICIjZGM2NWE0IiwKICAgICAgICAgICAgInBmIiA9ICIjMjQyNDVmIikKCiMjIGFkZCBhbHBoYSBmb3IgcGxvdHRpbmcKZGZfcGxvdCRhbHBoYVshZGZfcGxvdCRleHBlcmltZW50ID09ICJwbSJdIDwtIDAuNQpkZl9wbG90JGFscGhhW2RmX3Bsb3QkZXhwZXJpbWVudCA9PSAicG0iXSA8LSAxCmRmX3Bsb3QkYWxwaGFbZGZfcGxvdCRleHBlcmltZW50ID09ICJwZiJdIDwtIDEKCiMjcGxvdApzY21hcF9wY2EgPC0gZ2dwbG90KGRmX3Bsb3QsIGFlcyh4PVBDMl8zZCwgeSA9IFBDM18zZCxjb2xvdXI9ZXhwZXJpbWVudCwgYWxwaGEgPSBhbHBoYSkpICsgCiAgZ2VvbV9wb2ludCgpICsKICB0aGVtZV92b2lkKCkgKwogIHNjYWxlX2NvbG9yX21hbnVhbCh2YWx1ZXMgPSBjb2xvcnMpICsKICAjIyByZW1vdmUgYWxwaGEgc2NhbGUKICBzY2FsZV9hbHBoYV9jb250aW51b3VzKGd1aWRlPUZBTFNFKQogICMjIGRyYXcgcmluZyBhcm91bmQgZmllbGQgc2FtcGxlcwogICNnZW9tX3BvaW50KGRhdGE9ZGZfcGxvdFtkZl9wbG90JGV4cGVyaW1lbnQgPT0gInBtIiwgXSwgcGNoPTIxLCBmaWxsPU5BLCBzaXplPTIsIGNvbG91cj0iYmxhY2siLCBzdHJva2U9MSkgKwogICMjIG1ha2Ugbm9uLWZpZWxkIHNhbXBsZXMgbW9yZSBvcGFxdWUKICAjZ2VvbV9wb2ludChkYXRhPWRmX3Bsb3RbLWRmX3Bsb3QkZXhwZXJpbWVudCA9PSAicG0iLCBdLCBhbHBoYSA9IDAuMikKCiMjIHZpZXcKc2NtYXBfcGNhXzIgPC0gc2NtYXBfcGNhICsgCiAgZ3VpZGVzKGNvbG91cj1ndWlkZV9sZWdlbmQob3ZlcnJpZGUuYWVzID0gbGlzdChzaXplPTQpKSkKCnNjbWFwX3BjYV8yCmBgYAoKc2F2ZQpgYGB7cn0KI2dnc2F2ZSgiLi4vaW1hZ2VzX3RvX2V4cG9ydC9maWVsZF9zYW1wbGVzX3NjbWFwLnBuZyIsIHBsb3QgPSBzY21hcF9wY2FfMiwgZGV2aWNlID0gInBuZyIsIHBhdGggPSBOVUxMLCBzY2FsZSA9IDEsIHdpZHRoID0gMTUsIGhlaWdodCA9IDEwLCB1bml0cyA9ICJjbSIsIGRwaSA9IDMwMCwgbGltaXRzaXplID0gVFJVRSkKYGBgCgojIyBPdmVybGFwIGFuZCB2YWxpZGF0aW9uCgpgYGB7cn0KCmBgYAoKYGBge3J9CgpgYGAKCiMgMTEuIFNhdmUgYW5kIEV4cG9ydCB7LnRhYnNldH0KCiMjIHNhdmUKClNhdmUgZW52aXJvbm1lbnQKYGBge3J9CiMjIFRoaXMgc2F2ZXMgZXZlcnl0aGluZyBpbiB0aGUgZ2xvYmFsIGVudmlyb25tZW50IGZvciBlYXN5IHJlY2FsbCBsYXRlcgojc2F2ZS5pbWFnZShmaWxlID0gIkdDU0tPX21lcmdlLlJEYXRhIikKI2xvYWQoZmlsZSA9ICJHQ1NLT19tZXJnZS5SRGF0YSIpCmBgYAoKU2F2ZSBvYmplY3QocykKYGBge3J9CiMjIFNhdmUgYW4gb2JqZWN0IHRvIGEgZmlsZQpzYXZlUkRTKHRlbnguanVzdHd0LmludGVncmF0ZWQuc2V4LCBmaWxlID0gIi4uL2RhdGFfdG9fZXhwb3J0L3RlbnguanVzdHd0LmludGVncmF0ZWQuc2V4LlJEUyIpCiMjIFJlc3RvcmUgdGhlIG9iamVjdAojcmVhZFJEUyhmaWxlID0gIi4uL2RhdGFfdG9fZXhwb3J0L3RlbngubXV0YW50LmludGVncmF0ZWQuc2V4LlJEUyIpCgojIyBzYXZlIGludGVncmF0ZWQgb2JqZWN0IHRvIGZpbGUKc2F2ZVJEUyh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLCBmaWxlID0gIi4uL2RhdGFfdG9fZXhwb3J0L3RlbnguanVzdHd0LmludGVncmF0ZWQuUkRTIikgCiMjIHJlc3RvcmUgdGhlIG9iamVjdAojdGVueC5qdXN0d3QuaW50ZWdyYXRlZCA8LSByZWFkUkRTKCIuLi9kYXRhX3RvX2V4cG9ydC90ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkLlJEUyIpCgojIyBzYXZlIG1vbm9jbGUgb2JqZWN0CnNhdmVSRFMobW9ub2NsZS5vYmplY3QuYWxsLCBmaWxlID0gIi4uL2RhdGFfdG9fZXhwb3J0L21vbm9jbGUub2JqZWN0LmFsbC5SRFMiKSAKIyMgcmVzdG9yZSB0aGUgb2JqZWN0CiNtb25vY2xlLm9iamVjdC5hbGwgPC0gcmVhZFJEUygiLi4vZGF0YV90b19leHBvcnQvbW9ub2NsZS5vYmplY3QuYWxsLlJEUyIpCgojIyBzYXZlIGludGVncmF0ZWQgb2JqZWN0IHRvIGZpbGUKI3NhdmVSRFMoR0NTS09fbXV0YW50cywgZmlsZSA9ICIuLi9kYXRhX3RvX2V4cG9ydC9HQ1NLT19tdXRhbnRzLlJEUyIpIAojIyByZXN0b3JlIHRoZSBvYmplY3QKI3RlbnguanVzdHd0LmludGVncmF0ZWQgPC0gcmVhZFJEUygiLi4vZGF0YV90b19leHBvcnQvdGVueC5qdXN0d3QuaW50ZWdyYXRlZC5SRFMiKQoKIyMgZXh0cmFjdCBVTUFQIGNvb3JkaW5hdGVzIGFuZCBtZXRhZGF0YQojIyBleHRyYWN0IG1ldGFkYXRhCm1jYV93ZWJzaXRlX2RmIDwtIHRlbnguanVzdHd0LmludGVncmF0ZWRAbWV0YS5kYXRhCgojIyBhZGQgdW1hcCAtIG1lcmdlIG9uIHJvdyBuYW1lcyBhbmQgdGhlbiBtYWtlIHN1cmUgcm93IG5hbWVzIGFyZSBub3QgZHJvcHBlZCBvciBhZGRlZAptY2Ffd2Vic2l0ZV9kZiA8LSB0cmFuc2Zvcm0obWVyZ2UobWNhX3dlYnNpdGVfZGYsIHRlbnguanVzdHd0LmludGVncmF0ZWRAcmVkdWN0aW9uc1tbInVtYXAiXV1AY2VsbC5lbWJlZGRpbmdzLCBieT0wLCBhbGw9VFJVRSksIHJvdy5uYW1lcz1Sb3cubmFtZXMsIFJvdy5uYW1lcz1OVUxMKQoKIyMgc2VuZCB0aGlzIHRvIHN1bmlsOgojIGdncGxvdChtY2Ffd2Vic2l0ZV9kZiwgYWVzKHggPSBVTUFQXzEsIHkgPSBVTUFQXzIpKSArCiMgZ2VvbV9wb2ludChjb2wgPSBtY2Ffd2Vic2l0ZV9kZiRwdF9pZF9jb2xzKSArCiMgdGhlbWVfdm9pZCgpICsKIyBjb29yZF9maXhlZCgpCgojIyBleHBvcnQKd3JpdGUuY3N2KG1jYV93ZWJzaXRlX2RmLCBmaWxlID0gIi4uL2RhdGFfdG9fZXhwb3J0L21jYV93ZWJzaXRlX2RmLmNzdiIpCmBgYAoKQ2xlYW4gdXAKYGBge3J9CiNybShzczJfd3RfY2VsbHMpCiNybSh0ZW54Lmp1c3R3dC5pbnRlZ3JhdGVkKQojcm0odGVueC5qdXN0d3QubGlzdCkKYGBgCgojIyBmaW5hbCBmaWd1cmUgY29uc3RydWN0aW9uCgpbQ293cGxvdF0oaHR0cHM6Ly93aWxrZWxhYi5vcmcvY293cGxvdC9hcnRpY2xlcy9wbG90X2dyaWQuaHRtbCkocGxvdF9ncmlkKSwgW3BhdGNod29ya10oaHR0cHM6Ly9jcmFuLnItcHJvamVjdC5vcmcvd2ViL3BhY2thZ2VzL3BhdGNod29yay92aWduZXR0ZXMvcGF0Y2h3b3JrLmh0bWwpKHdyYXBfcGxvdHMpLCBhbmQgW2dncHVicl0oaHR0cDovL3d3dy5zdGhkYS5jb20vZW5nbGlzaC9hcnRpY2xlcy8yNC1nZ3B1YnItcHVibGljYXRpb24tcmVhZHktcGxvdHMvODEtZ2dwbG90Mi1lYXN5LXdheS10by1taXgtbXVsdGlwbGUtZ3JhcGhzLW9uLXRoZS1zYW1lLXBhZ2UvKSBjYW4gYWxsIGFsbG93IG11bHRpcGxlIHBsb3RzIHRvIGJlIHBsb3R0ZWQgdG9nZXRoZXIuIAoKYGBge3IsIGZpZy5oZWlnaHQgPSAxMS43LCBmaWcud2lkdGggPSA4LjN9CiMjIEEKIyB1bWFwX2lkX3B0CiMjIEIKIyBtYXJrZXIgZ2VuZSBleHByZXNzaW9uCiMjIEMKIyBNdXRhbnQgZ2VuZSBleHByZXNzaW9uCiMjIEQKIyBNb2R1bGVzCgojRmlndXJlX0EgPC0gZ3JpZC5hcnJhbmdlKGFycmFuZ2VHcm9iKFFDX2NvbXBvc2l0ZV9wbG90LCBRQ19taXRvX3Zpb2xpbiwgUUNfbWl0b19ncmFwaCwgUUNfYnlfZ2Vub3R5cGUsIG1hcHBpbmdfcmF0ZV9wbG90KSwgbnJvdz0zKSwgbnJvdz0yLCBoZWlnaHRzPWMoMTAsMikpCgojIyBjb3dwbG90IG1ldGhvZAojIyBjYW4gdXNlIHRoaXMgZm9yIGxhYmVsczogdG91cHBlcihsZXR0ZXJzKVsxOjEwXQoKIyMgQy4gTXV0YW50IGdlbmVzCm11dGFudF9nZW5lc19maWd1cmUgPC0gcGxvdF9ncmlkKAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAjIyBtYXJrZXIgZ2VuZXMgc3RhcnRzCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG1hcmtlcl9nZW5lX3Bsb3RfRkFNQiwKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbWFya2VyX2dlbmVfcGxvdF9NU1A4LAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBtYXJrZXJfZ2VuZV9wbG90X01TUDEsCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG1hcmtlcl9nZW5lX3Bsb3RfQVAyRywKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbWFya2VyX2dlbmVfcGxvdF9DQ1AyLAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBtYXJrZXJfZ2VuZV9wbG90X01HMSwKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIyMgbXV0YW50IGdlbmVzIHN0YXJ0cyAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG1hcmtlcl9nZW5lX3Bsb3RfZ2QxLAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBtYXJrZXJfZ2VuZV9wbG90X21kMSwKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbWFya2VyX2dlbmVfcGxvdF9tZDIsCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG1hcmtlcl9nZW5lX3Bsb3RfbWQzLAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBtYXJrZXJfZ2VuZV9wbG90X21kNCwgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG1hcmtlcl9nZW5lX3Bsb3RfbWQ1LAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBtYXJrZXJfZ2VuZV9wbG90X2ZkMSwKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbWFya2VyX2dlbmVfcGxvdF9mZDIsCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG1hcmtlcl9nZW5lX3Bsb3RfZmQzLAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBtYXJrZXJfZ2VuZV9wbG90X2ZkNCwgCiAgbGFiZWxfc2l6ZSA9IDEsIAogIG5yb3cgPSA0KQoKIyMgbGFiZWxzIGFzIHVwcGVyY2FzZSBsZXR0ZXJzOgojYyh0b3VwcGVyKGxldHRlcnMpWzI6MTddKQojIGxhYmVscyBhcyByb21hbiBudW1lcmFsczoKIyBjKHRvbG93ZXIoYXMucm9tYW4oYygyOjE3KSkKCkZpZ3VyZV9wdWJsaWNhdGlvbiA8LSBwbG90X2dyaWQodW1hcF9pZF9wdCArIHRoZW1lKHBsb3QubWFyZ2luID0gdW5pdChjKDAsIDAsIDAsIDApLCAiY20iKSksIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG11dGFudF9nZW5lc19maWd1cmUsCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIyMgYWRkIGVtcHR5IHBsb3QgdG8gZ2l2ZSBzcGFjaW5nCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgZ2dwbG90KCkgKyB0aGVtZV92b2lkKCksCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbGFiZWxzID0gYygnQScsICdCJyksIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGxhYmVsX3NpemUgPSAxMiwgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgbmNvbCA9IDIsIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIG5yb3c9MiwgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgcmVsX2hlaWdodHMgPSBjKDEsIDEsIDQpLCAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICByZWxfd2lkdGhzID0gYygxLCAyLCAzKSkKCkZpZ3VyZV9wdWJsaWNhdGlvbgpgYGAKCnNhdmUKYGBge3J9Cmdnc2F2ZSgiLi4vaW1hZ2VzX3RvX2V4cG9ydC9GaWd1cmVfQy5wbmciLCBwbG90ID0gRmlndXJlX3B1YmxpY2F0aW9uLCBkZXZpY2UgPSAicG5nIiwgcGF0aCA9IE5VTEwsIHNjYWxlID0gMSwgd2lkdGggPSAyMSwgaGVpZ2h0ID0gMjkuNywgdW5pdHMgPSAiY20iLCBkcGkgPSAzMDAsIGxpbWl0c2l6ZSA9IFRSVUUpCmBgYAoKYGBge3IsIGZpZy5oZWlnaHQgPSAxMS43LCBmaWcud2lkdGggPSA4LjN9CmxheW91dCA8LSAiCkFBQUJCQgpDQ0RERUUKRkZHR0hICklJSkpLSwoiCgpGaWd1cmVfcHVibGljYXRpb25fQSA8LSAoY29tcG9zaXRpb25fdW1hcF8xMHggKyB0aGVtZShwbG90LnRpdGxlID0gZWxlbWVudF90ZXh0KGhqdXN0ID0gMC41LCBmYW1pbHk9IkFyaWFsIiwgc2l6ZSA9IDEwKSkpICsKKGNvbXBvc2l0aW9uX3VtYXBfc3MyICsgdGhlbWUocGxvdC50aXRsZSA9IGVsZW1lbnRfdGV4dChoanVzdCA9IDAuNSwgZmFtaWx5PSJBcmlhbCIsIHNpemUgPSAxMCkpKSArIApwbG90X2xheW91dChuY29sID0gMikKCkZpZ3VyZV9wdWJsaWNhdGlvbiA8LSBGaWd1cmVfcHVibGljYXRpb25fQSArCiMoVU1BUF9ob28gKyB0aGVtZShwbG90LnRpdGxlID0gZWxlbWVudF90ZXh0KGhqdXN0ID0gMC41LCBmYW1pbHk9IkFyaWFsIiwgc2l6ZSA9IDEwKSkgKyBsYWJzKHRpdGxlPSJQcmVkaWN0ZWQgQXNleHVhbCBDeWNsZSBSZWFsIFRpbWVwb2ludCIpKSArCiMoVU1BUF9rYXNpYSArIHRoZW1lKHBsb3QudGl0bGUgPSBlbGVtZW50X3RleHQoaGp1c3QgPSAwLjUsIGZhbWlseT0iQXJpYWwiLCBzaXplID0gMTApKSArIGxhYnModGl0bGU9IlByZWRpY3RlZCBHYW1ldG9jeXRvZ2VuZXNpcyBSZWFsIFRpbWVwb2ludCIpKSArCiAgIyMgbWFya2VyIGdlbmVzCm1hcmtlcl9nZW5lX3Bsb3RfRkFNQiArCm1hcmtlcl9nZW5lX3Bsb3RfTVNQOCArCm1hcmtlcl9nZW5lX3Bsb3RfTVNQMSArCm1hcmtlcl9nZW5lX3Bsb3RfQ0RQSzUgKwptYXJrZXJfZ2VuZV9wbG90X0FQMkcgKwptYXJrZ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